Note: Descriptions are shown in the official language in which they were submitted.
INTELLIGENT ENERGY MANAGEMENT SYSTEM FOR DISTRIBUTED ENERGY
RESOURCES AND ENERGY STORAGE SYSTEMS USING MACHINE LEARNING
Field of the Disclosure
The present disclosure relates to methods and systems for managing electricity
demand, and
in particular for managing peaks in electricity demand. The disclosure further
relates to
forecasting production in photovoltaic cells.
Background to the Disclosure
Commercial and industrial (C&I) sites pay for power differently when compared
to residential
sites. In particular, C&I sites are generally billed for both their total
energy consumption and
their peak power demand, referred to as a "demand charge". FIG. 1 shows a
typical electricity
demand profile of a C&I site, also known as a demand charge profile. The
profile comprises a
substantially steady-state portion 12 and peaks 14 extending from steady-state
portion 12
above a demand threshold 16.
Demand charges exist as a mechanism for utilities to cover the costs of
delivering the desired
level of energy to customers. Each customer is assigned to a particular "rate
tariff' which
defines how demand charges are measured and assessed for that customer. While
details of
rate tariffs can vary from utility to utility, the demand charge is generally
based on the maximum
energy a site consumed during a time interval (for example 15 minutes or 1
hour) during the
previous billing cycle.
While there have been many advances in energy efficiency to enable C&I
customers to reduce
their energy consumption, until recently there have been few technologies for
reducing the
demand charge component of a customer's incurred cost. Furthermore, while
energy prices
have remained low in recent years, demand charges have been on the rise and
are expected
to continue to rise into at least the near future.
This presents the opportunity to minimize the demand charge component of the
customer's
incurred cost, referred to as "demand charge management". Not only does demand
charge
management reduce the customer's electricity bill, but it reduces risks
associated with
unmanaged peak loads and demand charge price escalation as rate tariffs are
updated.
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Date Recue/Date Received 2020-10-08
Non-grid electricity supplies, such as energy storage systems, have emerged as
a technology
which can enable demand charge management through a process known as "peak
shaving".
The basic process of peak shaving is accomplished by storing (charging) energy
in an energy
storage system at times of low energy demand, and discharging the stored
energy at times of
high energy demand.
The process of creating an accurate prediction of a site's future load is
difficult and complex,
as energy usage patterns can be highly variable from one site to another, and
can vary greatly
based on numerous factors including the time of day, the day of the week, the
day of the year,
weather, building type, work schedule, business processes, etc.
Given the complex nature of accurately forecasting future demand of a site,
errors in forecasting
may lead to insufficient battery capacity being available to deal with sudden
and unexpected
demand spikes. Given the possibility of forecasting errors leading to
unanticipated demand
spikes, it would be advantageous if off-grid electricity supply could be
better managed in order
to deal with unexpected demand spikes
Still further, in the context of energy demand management of a site, it is
known to predict or
forecast the production of photovoltaic (PV) cells at the site. PV cells may
be used, for example
in conjunction with other energy storage devices such as batteries, to provide
non-grid energy
for meeting demand spikes. Forecasting the production of PV cells is therefore
useful for
anticipating how much energy may be available from PV cells at the time a
demand spike is
anticipated.
Forecasting of PV cell production is known to be accomplished using a
forecasting model that
relies on knowledge of various physical parameters of the PV cells. While such
forecasting
may be relatively accurate in the absence of cloud cover, this type of
forecasting tends to break
down when cloud cover increases. It would therefore be advantageous if an
improved method
of forecasting PV cell production could be provided.
The present disclosure seeks to provide methods and systems that provide
improved
management of electricity demand charge, and improved methods and systems for
forecasting
PV cell production, in view of at least some of the deficiencies encountered
in the prior art.
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Date Recue/Date Received 2020-10-08
Summary of the Disclosure
According to a first aspect of the disclosure, there is provided a method of
reserving a capacity
of one or more energy storage devices, comprising: forecasting, based on past
electricity
demand of a site, future electricity demand of the site over a future time
period; determining a
forecasting error between the forecasted future electricity demand and an
actual electricity
demand of the site over the future time period; adjusting, based on the
forecasting error, a
target state of charge (SOC) of one or more energy storage devices; and
reserving, based on
the adjusted target SOC, a capacity of the one or more energy storage devices.
Adjusting the target SOC may be further based on a current SOC of the one or
more energy
storage devices.
Adjusting the target SOC may comprise using a proportional-integral-derivative
(PID) feedback
loop using the current SOC and the forecasting error as inputs to the PID
feedback loop.
Reserving the capacity of the one or more energy storage devices may comprise
increasing a
demand threshold below which an electricity demand of the site is met by one
or more grid-
based electricity sources, and above which the electricity demand is met by
the one or more
energy storage devices.
The method may further comprise adjusting the forecasting error based on one
or more
historical forecasting errors.
The method may further comprise, after increasing the demand threshold:
determining that the
electricity demand of the site has dropped below the demand threshold; and in
response thereto,
recharging the one or more energy storage devices. The method may further
comprise:
determining that the one or more energy storage devices are fully recharged;
and in response
thereto, decreasing the demand threshold.
Recharging the one or more energy storage devices may comprise recharging the
one or more
energy storage devices at a maximum rate. Recharging the one or more energy
storage
devices at a maximum rate may comprise setting the demand threshold equal to
the electricity
demand of the site.
Increasing the demand threshold may comprise increasing the demand threshold
based on a
rate of discharge of the one or more energy storage devices.
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Date Recue/Date Received 2020-10-08
Increasing the demand threshold may comprise increasing the demand threshold
based on a
magnitude of the electricity demand.
Forecasting the future electricity demand may comprise: obtaining past
electricity demand data
representing past electricity demand of the site over a past time period; and
forecasting, based
on the past electricity demand data, the future electricity demand. The past
time period may
extend from a past point in time to a current point in time.
Forecasting the future electricity demand may comprise inputting the past
electricity demand
data to a trained machine learning model comprised in a set of one or more
trained machine
learning models. The trained machine learning model to which is inputted the
past electricity
demand data may be selected based on the future time period.
The trained machine learning model may be configured to forecast future
electricity demand
data for a first time slot that immediately follows a current point in time.
The set of trained
machine learning models may comprise multiple trained machine learning models,
and each
other trained machine learning model may be configured to forecast future
electricity demand
data for a respective time slot that immediately follows a preceding one of
the time slots
The method may further comprise, prior to obtaining the past electricity
demand data: obtaining
electricity demand training data representing electricity demand of the site
over a training time
period greater than the past time period; and training each machine learning
model, using the
electricity demand training data, to forecast electricity demand of the site
over first time periods
as a function of electricity demand of the site over second time periods
preceding the first time
periods. The electricity demand training data may comprise data representing
one or more of:
weather; temperature; humidity; atmospheric pressure; months of a year; time
of day; dates;
days of a week; and whether or not a day of the week is a site holiday.
Each machine learning model may comprise one or more support vector machines
or a long
short-term memory model.
The past electricity demand data may further comprise data representing one or
more of:
weather; temperature; humidity; atmospheric pressure; months of a year; time
of day; dates;
days of a week; and the future time period.
According to a further aspect of the disclosure, there is provided a method of
forecasting
production of one or more photovoltaic cells, comprising: obtaining past
production data of one
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Date Recue/Date Received 2020-10-08
or more photovoltaic cells, wherein the past production data comprises
production data
representing, over a past time period, production as a function of weather of
the one or more
photovoltaic cells; and forecasting, based on the past production data, future
production data,
wherein the future production data comprises production data representing,
over a future time
period, future production as a function of future forecasted weather of the
one or more
photovoltaic cells.
Forecasting the future production data may comprise inputting the past
production data to a
trained machine learning model comprised in a set of one or more trained
machine learning
models. The trained machine learning model to which is inputted the past
production data may
be selected based on the future time period.
The trained machine learning model may be configured to forecast future
production data for a
first time slot that immediately follows a current point in time.
The set of trained machine learning models may comprise multiple trained
machine learning
models, and each other trained machine learning model may be configured to
forecast future
production data for a respective time slot that immediately follows a
preceding one of the time
slots.
The method may further comprise prior to obtaining the past production data:
obtaining
production training data representing production of the one or more
photovoltaic cells over a
training time period greater than the past time period; and training each
machine learning model,
using the production training data, to forecast, as a function of future
forecasted weather:
production of the one or more photovoltaic cells over first time periods as a
function of
production of the one or more photovoltaic cells over second time periods
preceding the first
time periods. The production training data may comprise data representing one
or more of:
weather; cloud cover; intensity of sunlight; temperature; humidity;
atmospheric pressure;
amount of precipitation; type of precipitation; wind speed; wind gusts; months
of a year; time of
day; dates; days of a week.
Each machine learning model may comprise one or more support vector machines
or a long
short-term memory model.
Date Recue/Date Received 2020-10-08
The past production data may further comprise data representing one or more
of: weather;
cloud cover; intensity of sunlight; temperature; humidity; atmospheric
pressure; months of a
year; time of day; dates; days of a week; and the future time period.
According to a further aspect of the disclosure, there is provided a method of
forecasting
production of one or more photovoltaic cells, comprising: forecasting,
according to one of a first
and a second forecasting model, future production of one or more photovoltaic
cells over a
future time period; determining a forecasting error between the forecasted
future production
and an actual production of the one or more photovoltaic cells over the future
time period; and
determining, based on the forecasting error, whether to transition the
forecasting to the other
of the first and second forecasting models.
Forecasting according to the first forecasting model may comprise forecasting
future production
based on one or more physical parameters of the one or more photovoltaic
cells. The one or
more physical parameters may comprise one or more: a size; an orientation; a
type; a quantity;
a model; an azimuth; a tilt; a latitude; a longitude; and an elevation.
The method may further comprise transitioning the forecasting to the other of
the first and
second forecasting models in response to determining that the forecasting
error is greater than
a preset threshold.
Forecasting according to the second forecasting model may comprise forecasting
future
production based on past production of the one or more photovoltaic cells as a
function of
weather. Forecasting according to the second forecasting model may further
comprise
forecasting future production further based on one or more physical parameters
of the one or
more photovoltaic cells. The forecasting of the future production based on
past production of
the one or more photovoltaic cells as a function of weather is further may be
based on an output
of the forecasting of the future production based on the one or more physical
parameters of the
one or more photovoltaic cells.
Forecasting according to the second forecasting model may further comprise:
obtaining past
production data representing, over a past time period, past production of the
one or more
photovoltaic cells as a function of weather; and forecasting, based on the
past production data,
future production data representing, over a future time period, future
production as a function
of future forecasted weather of the one or more photovoltaic cells.
Forecasting the future
production may comprise inputting the past production data to a trained
machine learning model
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Date Recue/Date Received 2020-10-08
comprised in a set of one or more trained machine learning models. The trained
machine
learning model to which is inputted the past production data may be selected
based on the
future time period.
The trained machine learning model may be configured to forecast future
production data for a
first time slot that immediately follows a current point in time.
The set of trained machine learning models may comprise multiple trained
machine learning
models, and each other trained machine learning model may be configured to
forecast future
production data for a respective time slot that immediately follows a
preceding one of the time
slots
The method may further comprise, prior to obtaining the past production data:
obtaining
production training data representing production of the one or more
photovoltaic cells as a
function of weather over a training time period greater than the past time
period; and training
each machine learning model, using the production training data, to forecast,
as a function of
future forecasted weather: production of the one or more photovoltaic cells
over first time
periods as a function of production of the one or more photovoltaic cells over
second time
periods preceding the first time periods. The production training data may
further comprise
data representing one or more of: weather; cloud cover; intensity of sunlight;
temperature;
humidity; atmospheric pressure; months of a year; time of day; dates; days of
a week.
Each machine learning model may comprise one or more support vector machines
or a long
short-term memory model.
The past production data may further comprise data representing one or more
of: weather;
cloud cover; intensity of sunlight; temperature; humidity; atmospheric
pressure; months of a
year; time of day; dates; days of a week; and the future time period.
According to a further aspect of the disclosure, there is provided a method of
training a machine
learning model, the method comprising: receiving at a machine learning model
production
training data representing, over a training time period, past production of
one or more
photovoltaic cells as a function of weather; and training the machine learning
model, using the
production training data, to forecast, as a function of future forecasted
weather: production of
the one or more photovoltaic cells over first time periods as a function of
production of the one
or more photovoltaic cells over second time periods preceding the first time
periods.
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Date Recue/Date Received 2020-10-08
According to a further aspect of the disclosure, there is provided a demand
management
system for managing electricity demand, the system comprising: one or more
energy storage
devices, such as one or more photovoltaic cells and/or one or more batteries;
and a control
system comprising one or more processors and memory having stored thereon
computer
program code configured, when executed by the one or more processors, to cause
the one or
more processors to perform any of the above-described methods.
According to a further aspect of the disclosure, there is provided a computer-
readable medium
having stored thereon computer program code configured, when executed by one
or more
processors, to cause the one or more processors to perform any of the above-
described
methods.
Brief Description of the Drawings
Detailed embodiments of the disclosure will now be described in connection
with the
accompanying drawings of which:
FIG. 1 shows a typical demand charge profile of a commercial/industrial site;
FIG. 2 is a schematic diagram of a power management system in accordance with
an
embodiment of the disclosure;
FIG. 3 is a more detailed schematic diagram of the power management system of
FIG. 2;
FIG. 4 is an example of a long short-term memory model in accordance with
embodiments of
the disclosure;
FIG. 5 is a flow diagram showing a method of managing electricity demand, in
accordance with
an embodiment of the disclosure;
FIGS. 6A and 6B are examples of feature vectors in accordance with embodiments
of the
disclosure; and
FIG 7 is a schematic diagram of a system for reserving battery capacity,
according to
embodiments of the disclosure;
FIG. 8 is a flow diagram of a method of reserving battery capacity, according
to embodiments
of the disclosure;
FIG. 9 is a plot of actual electricity demand vs. forecasted electricity
demand;
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Date Recue/Date Received 2020-10-08
FIGS. 10 and 11 show plots of actual electricity demand, forecasted
electricity demand, a
demand threshold, a metered load, aggregated state-of-charge, and target state-
of-charge,
according to embodiments of the disclosure;
FIG. 12 is a flow diagram of a method for forecasting photovoltaic cell
production, according to
embodiments of the disclosure;
FIG. 13 is a flow diagram of a method for forecasting photovoltaic cell
production, according to
embodiments of the disclosure; and
FIG. 14 is a plot of actual PV cell production vs. forecasted PV cell
production, according to
embodiments of the disclosure.
Detailed Description
The present disclosure seeks to provide methods and systems for managing
electricity demand,
and for forecasting production of photovoltaic cells. While various
embodiments of the
disclosure are described below, the disclosure is not limited to these
embodiments, and
variations of these embodiments may well fall within the scope of the
disclosure which is to be
limited only by the appended claims.
The word "a" or "an" when used in conjunction with the term "comprising" or
"including" in the
claims and/or the specification may mean "one", but it is also consistent with
the meaning of
"one or more", "at least one", and "one or more than one" unless the content
clearly dictates
otherwise. Similarly, the word "another" may mean at least a second or more
unless the content
clearly dictates otherwise.
The terms "coupled", "coupling" or "connected" as used herein can have several
different
meanings depending on the context in which these terms are used. For example,
the terms
coupled, coupling, or connected can have a mechanical or electrical
connotation. For example,
as used herein, the terms coupled, coupling, or connected can indicate that
two elements or
devices are directly connected to one another or connected to one another
through one or more
intermediate elements or devices via an electrical element, electrical signal
or a mechanical
element depending on the particular context. The term "and/or" herein when
used in
association with a list of items means any one or more of the items comprising
that list.
As will be appreciated by one skilled in the art, the various example
embodiments described
herein may be embodied as a method, system, or computer program product.
Accordingly, the
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Date Recue/Date Received 2020-10-08
various example embodiments may take the form of an entirely hardware
embodiment, an
entirely software embodiment (including firmware, resident software, micro-
code, etc.) or an
embodiment combining software and hardware aspects that may all generally be
referred to
herein as a "circuit", "module" or "system". Furthermore, the various example
embodiments
may take the form of a computer program product on a computer-usable storage
medium
having computer-usable program code embodied in the medium.
Any suitable computer-usable or computer readable medium may be used. The
computer-
usable or computer-readable medium may be, for example but not limited to, an
electronic,
magnetic, optical, electromagnetic, infrared, or semiconductor system,
apparatus, device, or
propagation medium. In the context of this document, a computer-usable or
computer-readable
medium may be any medium that can contain, store, communicate, propagate, or
transport the
program for use by or in connection with the instruction execution system,
apparatus, or device.
Computer program code for carrying out operations of various example
embodiments may be
written in an object oriented programming language such as Java, Smalltalk,
C++, Python, or
the like. However, the computer program code for carrying out operations of
various example
embodiments may also be written in conventional procedural programming
languages, such as
the "C" programming language or similar programming languages. The program
code may
execute entirely on a computer, partly on the computer, as a stand-alone
software package,
partly on the computer and partly on a remote computer or entirely on the
remote computer or
server. In the latter scenario, the remote computer may be connected to the
computer through
a local area network (LAN) or a wide area network (WAN), or the connection may
be made to
an external computer (for example, through the Internet using an Internet
Service Provider).
Various example embodiments are described below with reference to flow
diagrams and/or
block diagrams of methods, apparatus (systems) and computer program products
according to
example embodiments. It will be understood that each block of the flow
diagrams and/or block
diagrams, and combinations of blocks in the flow diagrams and/or block
diagrams, can be
implemented by computer program instructions. These computer program
instructions may be
provided to a processor of a general purpose computer, special purpose
computer, or other
programmable data processing apparatus to produce a machine, such that the
instructions,
which execute via the processor of the computer or other programmable data
processing
Date Recue/Date Received 2020-10-08
apparatus, create means for implementing the functions/acts specified in the
flow diagram
and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable
memory that
can direct a computer or other programmable data processing apparatus to
function in a
particular manner, such that the instructions stored in the computer-readable
memory produce
an article of manufacture including instructions which implement the
function/act specified in
the flow diagram and/or block diagram block or blocks.
The computer program instructions may also be loaded onto a computer or other
programmable
data processing apparatus to cause a series of operational steps to be
performed on the
computer or other programmable apparatus to produce a computer-implemented
process such
that the instructions which execute on the computer or other programmable
apparatus provide
steps for implementing the functions/acts specified in the flow diagram and/or
block diagram
block or blocks.
With reference to FIG. 2, there is shown a demand management system 20 used
for managing
electricity demand of a site 22, in accordance with an embodiment of the
disclosure. Site 22 is
electrically coupled to photovoltaic cells 24 via one or more inverters 23.
Site 22 is further
electrically coupled to an electricity grid 28 configured to provide
electricity on demand to site
22. One or more meters 21 are configured to monitor a consumption of
electricity at site 22.
Site 22 is further coupled to an energy management system processor 26
(described in further
detail below) and batteries 25 configured to provide stored electrical energy
to site 22. Batteries
25 are further electrically coupled to photovoltaic cells 24 for recharging of
batteries 25.
FIG. 3 shows another schematic representation of demand management system 20,
this time
showing communicative pathways between the various components of demand
management
system 20. Meters 21, inverters 23, and batteries 25 are communicatively
coupled to one or
more device communications modules 34 such that data from meters 21, inverters
23, and
batteries 25 may be communicated to a messaging bus 29 via device
communications modules
34. Also communicatively coupled to messaging bus 29 are a weather module 30
and a
forecasting module 31. Energy management system processor 26 is seen to
comprise a control
algorithm module 35, device communications modules 34, cloud gateway 32,
forecasting
module 31, weather module 30, and messaging bus 29, although in other
embodiments it shall
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Date Recue/Date Received 2020-10-08
be understood that energy management system processor 26 may comprise more or
fewer
modules.
Data from weather module 30 and forecasting module 31 may be transmitted along
messaging
bus 29 to control algorithm module 35. The data transmitted from weather
module 30
comprises any weather-related data which may have been forecasted by weather
module 30
using methods known to those of skill in the art, or which may have been
provided directly to
weather module 30 without weather module 30 performing the forecasting of the
weather.
Control algorithm module 35 comprises one or more processors communicative
with memory
having computer program code stored thereon. The program code is configured,
when
executed by the one or more processors, to perform any of the methods
described herein. In
particular, control algorithm module 35 is configured to implement one or more
peak shaving
algorithms, as described herein. Control algorithm module 35 may use data
received from
other components of demand management system 20, such as inverters 23,
batteries 25, and
forecasting module 31, in order to effectively implement the one or more peak
shaving
algorithms. Forecasting module 31 contains a machine learning model that is
used for
forecasting a future load (i.e. projected or expected electricity usage) at
site 22, as described
in further detail below.
In order to manage the electricity demand at site 22, control algorithm module
35 communicates
with forecasting module 31 which is configured to apply a trained machine
learning model to a
set of past demand data in order to forecast a future load at site 22. The
past demand data
comprises, amongst other data, past electricity usage data at site 22. As
described below, the
machine learning model is trained using demand training data which comprises
electricity
usage training data. The machine learning model may be trained by forecasting
module 31
itself or alternatively the trained machine learning model may be downloaded
to forecasting
module 31, for example via cloud gateway 32 communicating with an external
cloud 33. Thus,
the machine learning model may be trained externally to demand management
system 20, and
subsequently obtained by control algorithm module 35 through forecasting
module 31.
The machine learning model may be any machine learning model suitable for the
purposes
described herein. In some embodiments, the machine learning model is a support
vector
regression (SVR) model. In other embodiments, the machine learning model is a
long short-
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Date Recue/Date Received 2020-10-08
term memory (LSTM) model. Examples of an SVR model and an LSTM model that may
be
employed by forecasting module 31 are described below.
In a supervised regression problem, the training data is taken as {
, y1)} c x 91,
where denotes the space of the input patterns, for instance 91d. In
SV regression, the
goal is to find a function f (x) that has at most E deviation from the
actually obtained targets yi
for all the training data, and which is at the same time as flat as possible.
In the case of a linear
function f,
f (x) = < w, x> + b with co E b E R (1),
where <.,.> denotes the dot product in N. Flatness in (1) implies small to. In
order to achieve
flatness, it is required to minimize the Euclidean norm 116 112. Formally,
this can be written as
a convex optimization problem by requiring:
1
minimize ¨2 11w 112
subject tolyi¨< co, xi
(2).
co,xi > +b ¨ yi < s
An example LSTM model is shown in FIG. 4. In FIG. 4, Xi is a feature vector,
and 9i is
forecasted electricity usage. In the embodiment of FIG. 4, an example feature
vector X1 inputted
to an LSTM model comprises past electricity usage y at time step i-1, weather
at time step i,
and date and time at time step i, as per the following:
Xi = weatheri, date timed
In order to train the machine learning model, a set of demand training data is
used as an input
to the machine learning model. The demand training data comprises data
representing past
electricity usage at site 22. The past electricity usage may be determined for
example by
periodically obtaining meter readings from meters 21. In addition to past
electricity usage, the
demand training data comprises data representing a number of other different
parameters
related to electricity usage at site 22 over a period of time. For example,
the demand training
data may comprise data representing any prevailing weather conditions at site
22, for example
temperature, humidity, date and time information (for example information
relating to time of
day, day of the week, month, and whether not a day is a site holiday). Other
parameters may
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Date Recue/Date Received 2020-10-08
form part of the demand training data. The demand training data is preferably
obtained over a
relatively long period of time, for example two years.
Inputting the demand training data to the machine learning model trains the
machine learning
model to forecast electricity usage at site 22 as a function of past
electricity usage at site 22.
In other words, the machine learning model is able to determine possible
relationships between
past electricity usage (including past weather conditions and date/time
information) and future
electricity usage, by analyzing the demand training data to determine trends
within the demand
training data. Once the machine learning model has been trained using the
electricity usage
training data, the trained machine learning model may be used to forecast
future electricity
usage at site 22, by using known, past demand usage data.
FIG. 5 is a flowchart showing operations that may be taken by energy
management system
processor (EMSP) 36 in managing electricity demand at site 22, by performing
an electricity
demand management method 40. At block 41, EMSP 36 receives an instruction from
a user
of demand management system 20 to initiate a demand forecast, by performing
electricity
demand management method 40. The instruction specifies a future time period
over which
forecasting module 31 is to forecast future electricity usage. At block 42,
EMSP 36 obtains
past demand data. The past demand data comprises past electricity usage data
representing
past electricity usage at site 22. The past electricity usage data may be
obtained for example
by periodically obtaining meter readings from meters 21. In addition to past
electricity usage
data, the past demand data comprises data representing a number of other
different
parameters related to electricity usage at site 22 over a period of time. For
example, the past
demand data comprises data representing any prevailing weather conditions at
site 22,
temperature (for example temperature of batteries 25 as well as ambient
temperature),
atmospheric humidity, atmospheric pressure, and date and time information
representing the
particular future time period the user wishes to forecast. Other parameters
may form part of
the past demand data.
In some embodiments, the past demand data represents data over a one-week
period. In
addition, the period of time corresponding to the past demand data extends
from a past point
in time to a current point in time. In other words, the period of time
corresponding to the past
demand data extends from a past point in time to the point in time at which
EMSP 36 is
instructed to carry out electricity demand management method 40. Thus, the
past demand
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Date Recue/Date Received 2020-10-08
data may be obtained from a "rolling window" as time goes forward. In this
manner, more recent
demand data may be used as an input to the machine learning model, thereby
improving the
accuracy of the forecast.
At block 43, EMSP 36 accesses the trained machine learning model. As described
above, the
trained machine learning model may be downloaded to EMSP 36. Alternatively,
the trained
machine learning model may be stored on a device or devices external to EMSP
36, such that
EMSP 36 sends the past demand data to the external device or devices for
inputting to the
trained machine learning model, and receives from the external device or
devices output from
the trained machine learning model. Blocks 48 and 49 represent respectively
obtaining the
demand training data, as described above, and training the machine learning
model using the
demand training data.
At block 44, the past demand data is inputted to the trained machine learning
model. At block
45, the trained machine learning model outputs projected electricity usage
data representing
projected electricity usage at site 22 for the future time period selected by
the user.
In some embodiments, the user may request a forecast of the expected or
projected electricity
usage at site 22 for any amount of time up to the following 24 hours, with a
granularity of 15
minutes. Of course, in other embodiments the forecast may be extended to
longer or smaller
time horizons, with greater or smaller granularities. In order to perform the
forecasting, in one
embodiment the trained machine learning model uses 96 support vector machine
(SVR)
models. Each SVR model is configured to forecast projected electricity usage
for a specific
future time slot (i.e. a specific 15-minute tranche). For example, the first
SVR model is used to
forecast the immediately subsequent 15 minutes; in other words, the 15 minutes
that follow the
point in time that EMSP 36 is instructed to perform the forecast. The second
SVR model is
used to forecast the 15-30 minute time slot; in other words, the 15 minutes
that follow a point
in time 15 minutes after EMSP 36 is instructed to perform the forecast; etc.
By integrating
multiple ones of 96 forecasts of the 96 SVR models, a forecast horizon of 24
hours with 15-
minute granularity may be generated. As mentioned above, the number of SVR
models can
be tuned to forecast for different time horizons, and with different
granularity, and thus any
number of SVR models may in practice be used to forecast projected electricity
usage.
The past demand data is represented using feature vectors as described below.
Let d denote
the current day and j-1 denote the current time. The first SVR model is used
to forecast the
Date Recue/Date Received 2020-10-08
electricity usage of day d at time j-1+1. The second SVR model is used to
forecast the electricity
usage of day d at time j-1+2. More generally, the Mth SVR model is used to
forecast the
electricity usage of day d at time j-1+m. Each feature vector comprises data
relating to one or
more of the parameters identified above. For example, in addition to past
electricity usage,
each feature vector may comprise data relating to prevailing weather
conditions at site 22,
temperature (for example temperature of batteries 25 as well as ambient
temperature),
atmospheric humidity, atmospheric pressure, and date and time information
representing the
particular future time period the user wishes to forecast.
An example feature vector is shown below:
Load cooling heating extra humidity day of month holiday period to
heating week be
forecasted
Take for example the past electricity usage of the mth SVR model. The Mth SVR
model uses
as an input the load (past electricity usage) of time (j-1+m). Thus,
[load(0), load(1), load(15)] may be the load of day d-7 at time (j-1+m)-6,
(j-1+m)-5, (j-
1+m), (j-1+m)+1, (j-1+m)+9.
[load(16), load(17)] may be the load of day d-3 at time (j-1+m)-1, (j-1+m).
[load(18), load(19)] may be the load of day d-2 at time (j-1+m)-1, (j-1+m).
[load(20), load(21), load(25)] may be the load of day d-1 at time (j-1+m)-
6, (j-1+m)-1, (j-
1+m).
[load(26), load(27), load(121)] may be the load of day d at time j-96, j-
95, j-2, j-1 (i.e. all
the load / past electricity usage information of the past 24 hours).
Examples of load and cooling data in feature vectors are shown in FIGS. 6A and
6B. Note that
as mentioned above the feature vectors may comprise data relating to
additional parameters
(not shown in FIGS. 6A and 6B).
Once EMSP 36 has performed the forecast, at block 46, EMSP 36 identifies one
or more peaks
in the projected electricity usage. The peaks may be identified by comparing
the projected
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Date Recue/Date Received 2020-10-08
electricity usage to an electricity demand threshold (for example electricity
demand threshold
16 as can be seen in FIG. 1). There are various methods known in the art for
identifying such
peaks.
At block 47, EMSP 36 transmits one or more instructions for securing non-grid
electricity for
managing the projected electricity demand. In particular, EMSP 36 transmits
one or more
instructions for securing non-grid electricity for use during the future
periods corresponding to
the identified peaks. Non-grid electricity may be derived from various
distributed energy /
electricity resources, such as batteries 25 and/or photovoltaic cells 24, or
other on-site energy
generation (such as combined heat and power generation, or from a diesel/gas
generator).
During relatively steady-state electricity usage (such as during the period
corresponding to
steady-state demand 12 in FIG. 1), grid-based electricity may be used when
needed. However,
during periods of peak power demand (such as during the periods corresponding
to peaks 14
in FIG. 1), electricity from non-grid sources may be used so as to reduce the
overall cost to the
site owner.
EMSP 36 may be configured to take into account current electricity reserves in
non-grid
sources, such as in batteries 25 and/or photovoltaic cells 24, before
determining from which
non-grid source(s) to draw stored electricity so as to perform peak shaving.
Furthermore,
EMSP 36 may use the past demand data to determine the non-grid source for use
during the
periods of peak demand. In particular, the past demand data may also comprise
data
representing battery and photovoltaic cell storage over a past time period.
Using the trained
machine learning model, EMSP 36 may determine from the past demand data
projected battery
and photovoltaic cell storage over a future time period. Thus, by using past
battery and
photovoltaic cell storage data, EMSP 36 may predict future battery and
photovoltaic cell
storage. This information may be used by EMSP 36 to better anticipate from
which non-grid
source electricity is to be used for shaving the peaks, based on the amount of
stored electricity
in the non-grid sources.
EMSP 36 may further comprise different optimization routines for securing the
non-grid
electricity. Individual optimization routines may be selected by a user as a
function of what is
desired to be achieved. For example, if it is necessary to shave the peaks as
much as possible
without concern for completely draining the non-grid electricity sources, then
EMSP 36 may be
configured to instruct the drawing of as much electricity as allowable from
batteries 25 and
17
Date Recue/Date Received 2020-10-08
photovoltaic cells 24 during the peak demand periods. Alternatively, if it is
important to reserve
some non-grid electricity in case a sudden unexpected peak demand occurs, then
EMSP 36
may be configured to instruct the drawing of no more than a certain, preset
amount of electricity
from batteries 25 and/or photovoltaic cells 24 during the peak demand periods.
Reserving Battery Capacity
According to embodiments of the disclosure, it may be desirable to reserve a
capacity of
batteries 25, in order to mitigate the effect of unforeseen demand spikes. For
example, errors
in forecasting by forecasting module 31 may result in the failure to
anticipate or predict one or
more future demand spikes and thereby potentially expose the user to
additional demand
charges. In order to ensure that there remains some capacity within batteries
25 in order to
meet such unforeseen demand spikes, embodiments of the disclosure provide
methods and
systems that reserve a capacity of batteries 25, as now described in further
detail.
Turning to FIG. 7, there is shown an example of a system 70 for reserving a
capacity of batteries
25. System 70 includes a meter module 72, forecasting module 31 (which may be
the same
forecasting module 31 seen in FIG. 3), a battery system 74, and a proportional-
integral-
derivative (PID) control module 76 (which, according to some embodiments, may
be control
algorithm module 35 of FIG. 3). Meter module 72 obtains meter data (for
example data relating
to historical electricity demand) from meters 21 and provides the data to
forecasting module
31. As described in more detail below, forecasting module 31 is configured to
determine a
forecasting error and provide the forecasting error to PID control module 76.
Battery system
74 obtains battery data (for example data relating to a current state-of-
charge (SOC)) from
batteries 25 and provides the battery data to PID control module 76. PID
control module 76
uses the forecasting error and the current SOC provided by battery system 74
to determine a
capacity of batteries 25 that is to be reserved, as now described in more
detail. According to
some embodiments, data relating to energy stored in other renewable energy
sources (such as
photovoltaic cells) may also be provided to PID control module 76 (for example
by using data
obtained from inverters 23). Such data may additionally be used by PID control
module 76 to
determine a capacity of energy that is to be reserved in such other renewable
energy sources.
Turning to FIG. 8, there is shown a method 80 of reserving battery capacity,
according to
embodiments of the disclosure.
18
Date Recue/Date Received 2020-10-08
At block 81, future electricity demand of site 22 is forecasted. The future
electricity demand
may be forecasted using any of the above methods described in connection with
FIGS. 2-6B.
For example, forecasting module 31 may be configured to apply a trained
machine learning
model to a set of past demand data obtained from meter module 72, in order to
forecast a future
load at site 22. The past demand data comprises, amongst other data, past
electricity usage
data at site 22. The machine learning model is trained using demand training
data which
comprises electricity usage training data. The machine learning model may be
trained by
forecasting module 31 itself or alternatively the trained machine learning
model may be
downloaded to forecasting module 31, for example via cloud gateway 32
communicating with
an external cloud 33. Thus, the machine learning model may be trained
externally to demand
management system 20, and subsequently obtained by forecasting module 31.
The machine learning model may be any appropriate machine learning model
suitable for the
purposes described herein. In some embodiments, the machine learning model may
be a
support vector regression (SVR) model. In other embodiments, the machine
learning model
may be a long short-term memory (LSTM) model. Examples of an SVR model and an
LSTM
model that may be employed by forecasting module 31 are described above in
connection with
FIG. 4.
In order to train the machine learning model, a set of demand training data is
used as an input
to the machine learning model. The demand training data comprises data
representing past
electricity usage at site 22. The past electricity usage may be determined for
example by
periodically obtaining meter readings from meters 21. In addition to past
electricity usage, the
demand training data comprises data representing a number of other different
parameters
related to electricity usage at site 22 over a period of time. For example,
the demand training
data may comprise data representing any prevailing weather conditions at site
22, for example
temperature, humidity, date and time information (for example information
relating to time of
day, day of the week, month, and whether not a day is a site holiday). Other
parameters may
form part of the demand training data. The demand training data is preferably
obtained over a
relatively long period of time, for example two years.
Inputting the demand training data to the machine learning model trains the
machine learning
model to forecast electricity usage at site 22 as a function of past
electricity usage at site 22.
In other words, the machine learning model is able to determine possible
relationships between
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Date Recue/Date Received 2020-10-08
past electricity usage (including past weather conditions and date/time
information) and future
electricity usage, by analyzing the demand training data to determine trends
within the demand
training data. Once the machine learning model has been trained using the
electricity usage
training data, the trained machine learning model may be used to forecast
future electricity
usage at site 22, by using known, past demand usage data.
Using the past demand usage data, forecasting module 31 forecasts future
demand usage at
site 22, using the methods described above in connection with FIG. 5.
Returning to FIG. 8, at block 82, forecasting module 31 determines a
forecasting error. The
forecasting error may be an error between the forecasted future electricity
demand of site 22
and an actual electricity demand of site 22, over the same time period. In
other words, the
forecasting error is indicative of the inaccuracy of the forecast of the
future electricity demand.
The forecasting error may be adjusted based on one or more historical
forecasting errors. For
example, the forecasting error determined for a period during which
electricity demand tends
to be unpredictable may be adjusted based on one or more historical
forecasting errors
determined in the past for similar periods of time (e.g. for similar times of
day during which
electricity demand tends to be unpredictable). The forecasting error is
transmitted to PID
control module 79.
Turning to FIG. 9, there is shown a plot of actual demand 90 and forecasted
demand 92 over
time. As can be seen, forecasted demand 90 fails to anticipate a demand spike
94, leading to
a significant forecasting error during the period of demand spike 94.
Returning to FIG. 8, at block 83, PID control module 76 obtains from battery
system 74 the
current SOC of batteries 25. At block 84, PID control module 76 uses a PID
feedback to adjust
a target SOC of batteries 25. At block 85, based on the updated target SOC,
PID control
module 76 adjusts the demand threshold (the threshold above which electricity
demand is met
by non grid-based sources). For example, if the target SOC is increased, then
the demand
threshold is increased, thereby reserving a capacity of batteries 25.
Conversely, if the target
SOC is decreased, then the demand threshold is decreased, and thereby a
greater proportion
of demand is met through non grid-based sources, such as batteries 25 (which
generally
reduces the user's exposure to increased demand charges). An example of the
effect of
method 80 is now illustrated in connection with FIGS. 10 and 11.
Date Recue/Date Received 2020-10-08
Turning to FIG. 10, there is shown the plot of FIG. 9 with a demand threshold
96 overlaid
thereon. In addition, there is shown a plot of aggregated SOC 98 of batteries
25 and target
SO C 91 as a function of time. Target SO C 91 begins at a nominal 5%
(according to other
embodiments, target SOC 91 may begin at other values) and, as described in
further detail
below, may increase as the difference between actual electricity demand 90 and
forecasted
demand 92 increases. Thus, battery reserve is generally used as much as
possible to meet
electricity demand until uncertain loads are observed in which case battery
reserve is increased
in order to better manage the uncertain loads. A higher forecasting error
indicates lower
confidence in the forecast, and thus system 70 hedges against the uncertainty
by reserving
some battery capacity for future use.
At time t1, actual electricity demand 90 exceeds demand threshold 96,
resulting in aggregated
SO C 98 of batteries 25 reducing (i.e. batteries 25 begin to discharge) in
order to meet the
increase in electricity demand. Furthermore, at approximately t1, the
forecasting error begins
to increase as actual electricity demand 90 exceeds more and more forecasted
demand 92.
Thus, using method 80 described above, PID control module 74, in response to
detecting the
increase in forecasting error, causes target SOC 91 to increase. The increase
in target SOC
91 causes demand threshold 96 to begin increasing at time t2. A lag exists
between the
increase in target SOC 91 and the increase in demand threshold 96. For
example, PID control
module 74 may cause demand threshold 96 to increase only after identifying a
trend in the
forecasting error. As demand spike 94 ends, at time t3 the demand drops below
demand
threshold 96, and the demand is then met by grid-based sources as described
above, allowing
batteries 25 to be recharged as can be seen by aggregated SOC 98 in the lower
plot. In
addition, as actual demand 90 approaches forecasted demand 92, the forecasting
error
decreases, resulting to PID control module 76 decreasing target SOC 91.
Thus, by increasing demand threshold 96 in response to an increase in
forecasting error and/or
a decrease in the current SO C of batteries 25, a greater proportion of
electricity demand is met
by grid-based sources, ensuring an increased reserve of battery capacity for
any unexpected
demand peaks that may occur in the future.
FIG. 11 shows the same demand profile as that of FIG. 10 but additionally
shows the metered
demand 97 of the system (i.e. the grid-based electricity demand). The total
electricity demand
or consumption 90 is therefore the sum of metered demand 97 and the power
output 99 of
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Date Recue/Date Received 2020-10-08
batteries 25. In the embodiment of FIG. 11, as demand spike 94 causes
electricity demand 90
to increase above the demand threshold (not shown but corresponding to metered
demand
97), batteries 25 begin to discharge at time t1, as seen by trace 98
(representing the aggregated
SOC 98 of batteries 25). As the demand threshold increases, and as demand
spike 94 abates,
batteries 25 begin charging at t3 (when electricity demand 90 drops below the
demand
threshold). As electricity demand 90 remains below the demand threshold,
batteries 25 charge
at a maximum rate while keeping metered demand 97 at or below the demand
threshold. In
the case of FIG. 11, from t3 until the end of the charging, metered demand 97
is kept at the
demand threshold.
As can be seen from FIG. 11, during recharging of batteries 25, the demand
threshold is set to
metered demand 97, in order for batteries 25 to be recharged as quickly as
possible. A slower
charging rate is also possible, but the system is preferably configured to
recharge batteries 25
as quickly as possible (without exceeding the demand threshold), to thereby
provide maximum
battery reserve in as short an amount of time as possible. This may enable a
user to better
manage future unexpected demand spikes with reserve battery capacity. The
demand
threshold may be generally set to be the maximum monthly metered load.
Improved Photovoltaic Cell Forecasting
According to embodiments of the disclosure, the production of photovoltaic
(PV) cells 24 may
be forecasted using the above-described SVR model. In certain cases, such
forecasting may
represent an improvement over traditional methods of forecasting PV cell
production. Such
traditional methods generally rely on forecasting based on one or more
physical parameters of
the PV cells. While this may provide accurate forecasting during periods of
good weather, i.e.
with minimal or no cloud cover, the accuracy of such forecasting may decrease
as cloud cover
increases and it becomes more difficult to accurately predict future
production of the PV cells.
Turning to FIG. 12, there is shown a method of forecasting PV cell production,
according to
embodiments of the disclosure.
At block 122, forecasting module 31 obtains PV cell production training data,
to be used in
training a machine learning model. The machine learning model may be trained
by forecasting
module 31 itself or alternatively the trained machine learning model may be
downloaded to
forecasting module 31, for example via cloud gateway 32 communicating with an
external cloud
33. Thus, the machine learning model may be trained externally to demand
management
22
Date Recue/Date Received 2020-10-08
system 20, and subsequently obtained by forecasting module 31. The machine
learning model
may be any machine learning model suitable for the purposes described herein.
In some
embodiments, the machine learning model is a support vector regression (SVR)
model. In
other embodiments, the machine learning model is a long short-term memory
(LSTM) model.
Examples of an SVR model and an LSTM model that may be employed by forecasting
module
31 are described above in connection with FIG. 4.
At block 124, the machine learning model is trained. In order to train the
machine learning
model, the PV cell production training data is used as an input to the machine
learning model.
The PV cell production training data comprises data comprises data relating to
historical PV
cell production as a function of prevailing weather. The historical PV cell
production may be
determined for example by periodically obtaining readings from inverters 23.
In addition to
historical PV cell production data, the PV cell production training data
comprises data related
to prevailing weather conditions at site 22 during the period corresponding to
the historical PV
cell production. For example, such weather data may include data relating to
cloud cover,
intensity of sunlight, temperature, humidity, atmospheric pressure, amount of
precipitation, type
of precipitation, wind speed, wind gusts, months of a year, time of day,
dates, and days of a
week. Other parameters may form part of the PV cell production training data.
The weather
data may be provided by a third-party source. The PV cell production training
data is preferably
obtained over a relatively long period of time, for example two years.
Inputting the PV cell production training data to the machine learning model
trains the machine
learning model to forecast PV cell production at site 22 as a function of past
PV cell production
at site 22. In other words, the machine learning model is able to determine
possible
relationships between past PV cell production as a function of weather, and
future PV cell
production as a function of future forecasted weather, by analyzing the PV
cell production
training data to determine trends within the PV cell production training data.
Once the machine
learning model has been trained using the PV cell production training data,
the trained machine
learning model may be used to forecast future PV cell production at site 22,
by using known,
past PV cell production data.
At block 126, past PV cell production data is obtained. At block 128,
forecasting module 31
returns the forecasted PV cell production, by inputting the past PV cell
production data to the
23
Date Recue/Date Received 2020-10-08
trained machine learning model. In order to forecast PV cell production, any
of the methods
described above in connection with FIG. 5 may be used.
In particular, demand management system 20 first receives an instruction from
a user to initiate
a PV cell production forecast. The instruction specifies a future time period
over which
forecasting module 31 is to forecast PV cell production. The period of time
corresponding to
the past PV cell production extends from a past point in time to a current
point in time. In other
words, the period of time corresponding to the past PV cell production extends
from a past point
in time to the point in time at which EMSP 36 is instructed to perform the
forecasting. Thus,
the past PV cell production data may be obtained from a "rolling window" as
time goes forward.
In this manner, more recent PV cell production data may be used as an input to
the machine
learning model, thereby improving the accuracy of the forecast.
The past PV cell production data is inputted to the trained machine learning
model, and at block
128 the trained machine learning model outputs projected PV cell production
data representing
projected PV cell production at site 22 for the future time period selected by
the user.
In some embodiments, the user may request a forecast of the expected or
projected PV cell
production at site 22 for any amount of time up to the following 24 hours,
with a granularity of
15 minutes. Of course, in other embodiments the forecast may be extended to
longer or smaller
time horizons, with greater or smaller granularities. In order to perform the
forecasting, in one
embodiment the trained machine learning model uses 96 support vector machine
(SVR)
models. Each SVR model is configured to forecast projected electricity usage
for a specific
future time slot (i.e. a specific 15-minute tranche). For example, the first
SVR model is used to
forecast the immediately subsequent 15 minutes; in other words, the 15 minutes
that follow the
point in time that EMSP 36 is instructed to perform the forecast. The second
SVR model is
used to forecast the 15-30 minute time slot; in other words, the 15 minutes
that follow a point
in time 15 minutes after EMSP 36 is instructed to perform the forecast; etc.
By integrating
multiple ones of 96 forecasts of the 96 SVR models, a forecast horizon of 24
hours with 15-
minute granularity may be generated. As mentioned above, the number of SVR
models can
be tuned to forecast for different time horizons, and with different
granularity, and thus any
number of SVR models may in practice be used to forecast projected electricity
usage.
24
Date Recue/Date Received 2020-10-08
Thus, according to method 120, a machine learning model may be used to better
forecast PV
cell production. In particular, past PV cell production as a function of
weather may be used to
forecast future PV cell product as a function of future forecasted weather.
According to further embodiments of the disclosure, forecasting module 31 may
be configured
to forecast PV cell production according to more than one forecasting model,
and may be
configured to transition between multiple forecasting models. In particular,
forecasting module
31 may be configured to transition between a first forecasting module, in
which forecasting is
performed according to one or more physical parameters of PV cells 24, and a
second
forecasting model, in which forecasting is performed based at least partially
on historical data
relating to past PV cell production as a function of weather.
The one or more physical parameters of the first forecasting model may include
PV cell type,
PV cell quantity, PV cell model, azimuth, tilt, latitude, longitude, and
elevation. The first
forecasting model may use any of the methods described in Stein, Joshu S, et
al, "PVLIB: Open
Source Photovoltaic Performance Modeling Functions for Matlab and Python",
Sandia National
Lab. (SNL-NM), Albuquerque, NM (United States), May 1, 2016, which is herein
incorporated
by reference in its entirety.
Turning to FIG. 13, according to embodiments of the disclosure, there is shown
a method of
forecasting PV cell production based on multiple forecasting models.
At block 132, forecasting module 31 forecasts future PV cell production
according to the first
forecasting model. For example, forecasting module 31 forecasts future PV cell
production
using any of the methods described in PVLIB: Open Source Photovoltaic
Performance
Modeling Functions for Matlab and Python. At block 134, forecasting module 31
determines a
forecasting error between the forecasted PV cell production and actual PV cell
production. If
the forecasting error becomes too large, then at block 136 forecasting module
31 may
determine that the current (first) forecasting model is ineffectively
forecasting PV cell
production. Thus, forecasting module 31 may transition from the first
forecasting model to the
second forecasting model.
For example, as described above, the first forecasting model which relies on
physical
parameters of PV cells 25 is generally accurate to the extent that cloud cover
is nonexistent or
minimal. If cloud cover increases sufficiently, the forecasting error will
increase until forecasting
module 31 determines that forecasting should now proceed on the basis of the
second
Date Recue/Date Received 2020-10-08
forecasting model. According to the second forecasting model, PV cell
production is forecasted
according to a hybrid approach of the first, physical forecasting model and
the machine
learning-based model described above in connection with FIG. 12. Thus,
according to the
second forecasting model, PV cell production is forecasted based on a
combination of one or
more physical parameters of PV cells 25 (using for example the methods
described in PVLIB:
Open Source Photovoltaic Performance Modeling Functions for Matlab and Python)
and
historical data relating to past PV cell production of PV cells 25 as a
function of weather. In
particular, PV cell production is first forecasted based on one or more
physical parameters of
PV cells 25. Subsequently, the output of the forecasting according to the
first forecasting model
is then used as an input to the machine learning-based model described above
in connection
with FIG. 12.
According to the second, hybrid forecasting model, the forecasting error may
be small when
there is significant cloud cover. However, when cloud cover decreases
sufficiently, the
forecasting error will increase, and the forecasting module 31 may then
transition back to the
first forecasting model.
FIG. 14 shows an example plot of actual PV cell production 144, forecasted PV
cell production
142 based on the first, physical forecasting model, and forecasted PV cell
production 146 based
on the second, hybrid forecasting model. FIG. 14 shows that the second, hybrid
forecasting
model provides an improved method for estimating PV cell production.
While the disclosure has been described in connection with specific
embodiments, it is to be
understood that the disclosure is not limited to these embodiments, and that
alterations,
modifications, and variations of these embodiments may be carried out by the
skilled person
without departing from the scope of the disclosure. For example, it is
contemplated that the
electricity management system may be configured to control the energy demand
of individual
energy-demanding devices at the site, so as to better manage the energy demand
curve. It is
furthermore contemplated that any part of any aspect or embodiment discussed
in this
specification can be implemented or combined with any part of any other aspect
or embodiment
discussed in this specification.
26
Date Recue/Date Received 2020-10-08
