Load Forecasting

POWER SYSTEM PLANNING AND LOAD
FORECASTING
MODULE I-LOAD FORECASTING
LINSS T ALEX
ASSISTANT PROFESSOR
DEPARTMENT OF EEE
MET’S SCHOOL OF ENGINEERING,MALA

Load forecasting
Electrical energy has to be generated whenever there is
a demand for it. It is, therefore, imperative for the
electric power utilities that the load on their systems
should be estimated in advance. This estimation of load
in advance is commonly known as load forecasting. It is
necessary f or power system planning.

• Power system expansion planning starts with a
forecast of anticipated future load requirements.
• The estimation of both demand and energy
requirements is crucial to an effective system
planning.
• Demand predictions are used for determining the
generation capacity, transmission, and distribution
system additions, etc.
• Load forecasts are also used to establish
procurement policies for construction capital energy
forecasts, which are needed to determine future fuel
requirements. Thus, a good forecast, reflecting the
present and future trends, is the key to all planning.

• The term forecast refers to projected load
requirements determined using a systematic
process of defining future loads in sufficient
quantitative detail to permit important system
expansion decisions to be made.
• Unfortunately, the consumer load is
essentially uncontrollable although minor
variations can be affected by frequency
control and more drastically by load shedding.
• The variation in load does exhibit certain daily
and yearly pattern repetitions and an analysis
of these forms the basis of several load-
prediction techniques.

• Electrical Load Forecasting is the estimation for
future load by an industry or utility company.
Load forecasting is vitally important for the
electric industry in the deregulated economy.
• A large variety of mathematical methods have
been developed for load forecasting. It has many
applications including energy purchasing and
generation, load switching, contract evaluation,
and infrastructure development.
• Now a day, development in every sector is a
heading at a very rapid pace and in the same
pattern, the demand for power is also growing.
While speaking about electrical power, it is
important to understand that it has three main
sectors i.e. generation, transmission and
distribution.

• Electrical power generated by any source is then
transmitted through transmission lines at
different voltage level and then distributed to
different categories of consumers later on.
• It is not as simple as described in few words but
every stage is a complete independent system in
itself. Effective load forecasts can help to improve
and properly plan these three fields of power
systems.
• Accurate models for electric power load
forecasting are essential to the operation and
planning of a utility company.

• Load forecasting helps an electric utility to
make important decisions including decisions
on purchasing and generating electric power,
load switching, and infrastructure
development.
• Load forecasts are extremely important for
energy suppliers, ISOs, financial institutions,
and other participants in electric energy
generation, transmission, distribution, and
markets.

• Over the past decade, many western nations have
begun major structural reforms of their electricity
markets.
• These reforms are aimed at breaking up traditional
regional monopolies and replacing them with
several generation and distribution utilities that bid
to sell or buy electricity through a wholesale
market.
• While the rules of how various wholesale markets
operate differ, in each case it is hoped that the end
result is a decline in the price of electricity to end
users and a price that better reflects the actual
costs involved.

• Load forecasting is however a difficult task. First,
because the load series is complex and exhibits
several levels of seasonality: the load at a given
hour is dependent not only on the load at the
previous hour, but also on the load at the same
hour on the previous day, and on the load at the
same hour on the day with the same
denomination in the previous week.

• Secondly, there are many important exogenous
variables that must be considered, especially
weather-related variables.
• It is relatively easy to get forecast with about 10
% mean absolute error; however, the cost of
error are so high that research could help to
reduce it in a few percent points would be amply
justified .

Types of forecasting
Short term forecasts
(one hour to a week)
Medium forecasts
(a month up to a year)
Long term forecasts
(over one year)
Load Forecasts

Long term load forecasting (LTLF): Applicable
for system and long term network planning.
Basically two approaches are available for this
purpose.
(a) Peak Load Approach
In this case, the simplest approach is to find the
trend curve, which is obtained by plotting the past
values of annual peaks against years of operation.

(b) Energy Approach
• Another method is to forecast annual energy
sales to different classes of customers like
residential, commercial, industrial, etc., which
can then be converted to annual peak demand
using the annual load factor.
• A detailed estimation of factors such as rate of
house building, sale of electrical appliances,
growth in industrial and commercial activities
are required in this method.

2. Midterm Load Forecasting (MTLF):
Applicable for quarterly, half yearly and yearly
LF needs.
3. Short term Load Forecasting (STLF):
Applicable for day ahead and week ahead LF
needs.

Applications of STLF are mainly:
• To drive the scheduling functions that decides the
most economic commitment of generation
sources.
• To access the power system security based on the
information available to the dispatchers to prepare
the necessary corrective actions.
• To provide the system dispatcher with the latest
weather predictions so that the system can be
operated both economically and reliably

Need (purpose) of load forecasting
1) For proper planning of power system.
• To determine the potential need for additional new
generating facilities
• To determine the location of units.
• To determine the size of plants.
• To determine the year in which they are required.
• To determine that they should provide primary peaking
capacity or energy or both.
• To determine whether they should be constructed and
owned by the Central Government or State
Government or Electricity Boards or by some other
autonomous corporations

2) For Proper Planning of Transmission and
Distribution Facilities
• For planning the transmission and distribution
facilities, the load forecasting is needed so
that the right amount of power is available at
the right place and at the right time.
• Wastage due to misplanning like purchase of
equipment, which is not immediately
required, can be avoided.

3) For Proper Power System Operation
• Load forecast based on correct values of
demand will prevent overdesigning of
conductor size, etc. as well as overloading of
distribution transformers and feeders.
• Thus, they help to correct voltage, power
factor, etc. and to reduce the losses in the
distribution system.

4) For Proper Financing
The load forecasts help the Boards to estimate
the future expenditure, earnings, and returns
and to schedule its financing program
accordingly.

5) For Proper Manpower Development
Accurate load forecasting annually reviewed
will come to the aid of the Boards in their
personnel and technical manpower planning
on a long-term basis.
Such a realistic forecast will reduce
unnecessary expenditure and put the Boards
finances on a sound and profitable footing.

6) For Proper Grid Formation
• Interconnections between various state grids
are now becoming more and more common.
• The expensive high-voltage interconnections
must be based on reliable load data,
otherwise the generators connected to the
grid may frequently fall out of step causing
power to be shut down.

7) For Proper Electrical Sales
• Proper planning and the execution of
electrical sales program are aided by proper
load forecasting

Accuracy of Electrical load forecasting
• Accurate models for electric power load
forecasting are essential to the operation and
planning of a utility company.
• Load forecasting helps an electric utility to make
important decisions including decisions on
purchasing and generating electric power, load
switching, and infrastructure development.
• For a particular region, it is possible to predict
the next day load with an accuracy of
approximately 1-3%.

• However, it is impossible to predict the next year
peak load with the similar accuracy since accurate
long-term weather forecasts are not available.
• For the next year peak forecast, it is possible to
provide the probability distribution of the load
based on historical weather observations.
• It is also possible, according to the industry
practice, to predict the so-called weather
normalized load, which would take place for
average annual peak weather conditions or worse
than average peak weather conditions for a given
area.

• Weather normalized load is the load calculated for
the so-called normal weather conditions which are
the average of the weather characteristics for the
peak historical loads over a certain period of time.
• The duration of this period varies from one utility to
another.
• Load forecasting has always been important for
planning and operational decision conduct by utility
companies.
• However, with the deregulation of the energy
industries, load forecasting is even more important.
• With supply and demand fluctuating and the changes
of weather conditions and energy prices increasing
by a factor of ten or more during peak situations,
load forecasting is vitally important for utilities.

• Short-term load forecasting can help to estimate
load flows and to make decisions that can prevent
overloading.
• Timely implementations of such decisions lead to
the improvement of network reliability and to the
reduced occurrences of equipment failures and
blackouts.
• Load forecasting is also important for contract
evaluations and evaluations of various sophisticated
financial products on energy pricing offered by the
market.

• Most forecasting methods use statistical techniques
or artificial intelligence algorithms such as
regression, neural networks, fuzzy logic, and expert
systems.
• Two of the methods, so-called end-use and
econometric approach are broadly used for
medium- and long-term forecasting.
• A variety of methods, which include the so-called
similar day approach, various regression models,
time series, neural networks, statistical learning
algorithms, fuzzy logic, and expert systems, have
been developed for short-term forecasting.

• The development and improvements of appropriate
mathematical tools will lead to the development of
more accurate load forecasting techniques.
• The accuracy of load forecasting Load Forecasting
depends not only on the load forecasting
techniques, but also on the accuracy of forecasted
weather scenarios.
• Important Factors for Forecasts For short-term load
forecasting several factors should be considered,
such as time factors, weather data, and possible
customers’ classes.

• The medium- and long-term forecasts take into
account the historical load and weather data, the
number of customers in different categories, the
appliances in the area and their characteristics
including age, the economic and demographic data
and their forecasts, the appliance sales data, and
other factors.
• The time factors include the time of the year, the
day of the week, and the hour of the day

• There are important differences in load between
weekdays and weekends.
• The load on different weekdays also can behave
differently.
• For example, Mondays and Fridays being adjacent
to weekends, may have structurally different loads
than Tuesday through Thursday.
• This is particularly true during the summer time.

• Holidays are more difficult to forecast than non-
holidays because of their relative infrequent
occurrence.
• Weather conditions influence the load. In fact,
forecasted weather parameters are the most
important factors in short-term load forecasts.
• Various weather variables could be considered for
load forecasting.
• Temperature and humidity are the most commonly
used load predictors.

Factors for accurate forecasts
Weather influence
Time factors
Customer classes

Weather Influence
• Electric load has an obvious correlation to
weather. The most important variables responsible
in load changes are:
• Dry and wet bulb temperature
• Dew point
• Humidity
• Wind Speed / Wind Direction
• Sky Cover
• Sunshine

Time factors
• In the forecasting model, we should also
consider time factors such as:
• The day of the week
• The hour of the day
• Holidays

Customer Class
• Electric utilities usually serve different types of
customers such as residential, commercial,
and industrial

Forecasting techniques
 Qualitative Approaches to Forecasting
 Quantitative Approaches to Forecasting
 The Components of a Time Series
 Using Smoothing Methods in Forecasting
 Measures of Forecast Accuracy
 Using Trend Projection in Forecasting
 Using Regression Analysis in Forecasting
 An essential aspect of managing any organization is
planning for the future.
 Organizations employ forecasting techniques to
determine future inventory, costs , capacities, and
interest rate changes.

Qualitative Approaches to Forecasting
Delphi Approach
• A panel of experts, each of whom is physically
separated from the others and is anonymous, is
asked to respond to a sequential series of
questionnaires.
• After each questionnaire, the responses are
tabulated and the information and opinions of the
entire group are made known to each of the other
panel members so that they may revise their
previous forecast response.
• The process continues until some degree of
consensus is achieved.

Scenario Writing
• Scenario writing consists of developing a
conceptual scenario of the future based on a
well defined set of assumptions.
• After several different scenarios have been
developed, the decision maker determines
which is most likely to occur in the future and
makes decisions accordingly.

Subjective or Interactive Approaches
• These techniques are often used by committees
or panels seeking to develop new ideas or solve
complex problems.
• They often involve "brainstorming sessions".
• It is important in such sessions that any ideas or
opinions be permitted to be presented without
regard to its relevancy and without fear of
criticism.

Quantitative Approaches to Forecasting
• Quantitative methods are based on an analysis of
historical data concerning one or more time series.
• A time series is a set of observations measured at
successive points in time or over successive periods
of time.
• If the historical data used are restricted to past
values of the series that we are trying to forecast, the
procedure is called a time series method.
• If the historical data used involve other time series
that are believed to be related to the time series that
we are trying to forecast, the procedure is called a
causal method.
• Quantitative approaches are generally preferred.

Time Series Data
• Time Series Data is usually plotted on a graph to
determine the various characteristics or
components of the time series data.
• There are 4 Major Components : Trend, Cyclical,
Seasonal, and Irregular Components.
• The trend component accounts for the gradual
shifting of the time series over a long period of
time.
• Any regular pattern of sequences of values above
and below the trend line is attributable to the
cyclical component of the series.

• The seasonal component of the series accounts for
regular patterns of variability within certain time
periods, such as over a year.
• The irregular component of the series is caused by
short-term, unanticipated and non-recurring factors
that affect the values of the time series. One cannot
attempt to predict its impact on the time series in
advance.

• In time series data we can learn the following
Forecasting Approaches:
Smoothing Methods
Trend Projections
• The time series is fairly stable and has no
significant trend, seasonal, or cyclical effects,
one can use smoothing methods to average
out the irregular components of the time
series.

Three common smoothing methods are:
• Moving average
• Weighted moving average
• Exponential smoothing

Moving Average Method
The moving average method consists of computing
an average of the most recent n data values for the
series and using this average for forecasting the value
of the time series for the next period.

Weighted Moving Average Method
The weighted moving average method consists of computing
a weighted average of the most recent n data values for the
series and using this weighted average for forecasting the
value of the time series for the next period.
The more recent observations are typically given more weight
than older observations.
For convenience, the weights usually sum to 1.
The regular moving average gives equal weight to past data
values when computing a forecast for the next period.
The weighted moving average allows different weights to be
allocated to past data values.
There is no excel command for computing this so you must do
this manually.
 You can either manually enter the formulas into excel and
apply to all periods or compute value by hand.

Exponential Smoothing
Using exponential smoothing, the forecast for
the next period is equal to the forecast for the
current period plus a proportion (a) of the
forecast error in the current period.
Using exponential smoothing, the forecast is
calculated by:
Ft+1=a Yt + (1- a)Ft
This is the same as
Ft+1 = Ft + α (Yt – Ft)

 a is the smoothing constant (a number
between 0 and 1)
 Ft is the forecast for period t
 Ft +1 is the forecast for period t+1
 Yt is the actual data value for period t

Explanatory forecasting
• Explanatory models assume that variable to be
forecasted exhibits an explanatory relationship
with one or more independent variables.
GNP=f(monetary and fiscal policies,inflation,capital
spending,imports,exports,error)
• Relationship is not exact.
• There will always be changes in GNP that cannot
be accounted by the variables in the model and
thus some part of GNP will remains
unpredictable.

• Therefore it includes the error term on the right
which represents random effects, beyond the
variable in the model, that affect the GNP figures.
• Explanatory models can be applied to many
systems-a national economy, a company’s market
on a household.
• The purpose of explanatory model is to discover
the form of relationship and using it to forecast
future values of forecast variables.
• According to explanatory forecasting change in
input will affect the output of the system in a
predictable way, assuming the explanatory will
not change.

Least-squares estimates
• The method uses an operator that controls one
variable at a time.
• An optimal starting point is determined using the
operator.
• This method utilizes the autocorrelation function and
the partial autocorrelation function of the resulting
differenced past load data in identifying a suboptimal
model of the load dynamics.
• The weighting function, the tuning constants and
the weighted sum of the squared residuals form a
three-way decision variable in identifying an optimal
model and the subsequent parameter estimates.

• Consider the parameter estimation problem
involving the linear measurement equation:
• where Y is an n x 1 vector of observations, X is an
n x p matrix of known coefficients (based on
previous load data), β is a p x 1 vector of the
unknown parameters and ε is an n x 1 vector of
random errors.
• Results are more accurate when the errors are
not Gaussian. β can be obtained by iterative
methods (Mbamalu and El- Hawary 1992).
• Given an initial β , one can apply the Newton
method.

Trend Analysis
• The trend extrapolation method uses the information of the
past to forecast the load of the future.
• A simple example is shown in figure(2010), in which load is
shown for the last 10 years and predicted to be 2906 MW in
2015.

• A curve fitting approach may be employed to find
the load of the target year.
• This approach is simple to understand and
inexpensive to implement.
• However, it implicitly assumes that the trends in
various load driving parameters remain unchanged
during the study period.

Regression Analysis
• Regression Analysis is similar to trend analysis,
except the independent variable is not restricted
to time.
• Instead of letting time represent our
independent variable, we can forecast
• For this model, we would find the regression
equation in the same manner in which we found
the trend line except we would call the
independent variable x, instead of t.

• Using the method of least squares, the formula for the
regression line is:
Y = b0 + b1x.
where: Y= dependent variable which depends on the
value of x
b1 = slope of the regression line
b0 = regression line projection for x= 0
• The dependent variable Y can predict using the same
forecast function in Excel as used to forecast a trend line.

Peak Load forecasting
• Annual peak load forecasts are important for
planning and, in particular, for securing adequate
generation, transmission and distribution
capacities.
• More accurate peak load forecasts improve decision
making capabilities in capital expenditures and
improve reliability of the system.
• Future peak load is not deterministic and it
depends on several uncertain factors including
weather conditions.

Box Jenkins time series method
In time series analysis, the Box–Jenkins method, named
after the statisticians George Box and Gwilym Jenkins,
applies autoregressive moving average ARMA or ARIMA
models to find the best fit of a time-series model to past
values of a time series.
The Box-Jenkins approach is one of the most widely used
methodologies for the analysis of time-series data.
It is popular because of its generality; it can handle any
series, stationary or not, with or without seasonal elements,
and it has well-documented computer programs.
Although Box and Jenkins have been neither the originators
nor the most important contributors in the field of Auto
Regressive Moving Average(ARMA) models.
They have popularized these models and made them readily
accessible to everyone, so much that ARMA models are
sometimes referred to as Box-Jenkins models.

The basic steps in the Box-Jenkins methodology are:-
(1) Differencing the series so as to achieve stationarity
(2) Identification of a tentative model
(3) Estimation of the model
(4)Diagnostic checking (if the model is found
inadequate, we go back to step (2)
(5) Using the model for forecasting and control.

1. Differencing to achieve stationarity: How do we
conclude whether a time series is stationary or not?
 We can do this by studying the graph of the
correlogram of the series.
 The correlogram of a stationary series drops off as k,
the number of lags, becomes large, but this is not
usually the case for a nonstationary series.
 Thus the common procedure is to plot the
correlogram of the given series , successive
differences and so on, and look at the correlograms
at each stage.
 We keep differencing until the correlogram dampens

2. Once we have used the differencing procedure to get
a stationary time series, we examine the correlogram
to decide on the appropriate orders of the AR and MA
components.
 The correlogram of a MA process is zero after a point.
 That of an AR process declines geometrically. The
correlograms of ARMA processes show different
patterns (but all dampen after a while).
 Based on these, one arrives at a tentative ARMA model.
 This step involves more of a judgmental procedure than
the use of any clear-cut rules.

3. The next step is the estimation of the tentative
ARMA model identified in step 2. We have
discussed in the preceding section the estimation
of ARMA models.
4. The next step is diagnostic checking to check the
adequacy of the tentative model. We discussed in
the preceding section the Q and Q* statistics
commonly used in diagnostic checking.
5. The final step is forecasting

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