The document discusses methods of short term, medium term, and long term load forecasting. It explains that load forecasts help electric utilities make important planning decisions. Short term forecasts are from 1 hour to 1 week, medium from 1 week to 1 year, and long term over 1 year. Factors that influence forecasts include time, weather, customer classes, historical load data, economic data, and appliance characteristics. Common forecasting methods include regression models, time series, neural networks, and end-use or econometric approaches. Accurate load forecasting is essential for utility operation and planning.

1. STANI MEMORIAL COLLEGE OF ENGINEERING & TECHNOLOGY
POWER SYSTEM DESIGN LAB
EXPERIMENT NO. 4
Aim: - Methods of short term, medium term and long term load forecasting.
Apparatus Requirements: - Transmission System, SCADA System.
Theory: - 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.
Load forecasts can be divided into three categories: short-term forecasts which are usually
from one hour to one week, medium forecasts which are usually from a week to a year, and
long-term forecasts which are longer than a year. The forecasts for different time horizons are
important for different operations within a utility company. The natures of these forecasts are
different as well. For example, 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

2. loads over a certain period of time. The duration of this period varies from one utility to
another. Most companies take the last 25-30 years of data.
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.
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 mediumand 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.
Forecasting Methods: - Over the last few decades a number of forecasting methods have
been developed. 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,
expert systems, fuzzy logic, and statistical learning algorithms, are used for short-term
forecasting. The development, improvements, and investigation of the appropriate
mathematical tools will lead to the development of more accurate load forecasting
techniques.

3. Statistical approaches usually require a mathematical model that represents load as function
of different factors such as time, weather, and customer class. The two important categories
of such mathematical models are: additive models and multiplicative models. They differ in
whether the forecast load is the sum (additive) of a number of components or the product
(multiplicative) of a number of factors.
Long Term Load Forecasting: - It takes a pretty long time to plan, additions generating
capacity, generating system expansion planning start with forecast of anticipated future load
requirement.
1. Should the maximum demand be forecast using energy and load factors.
2. Should the total forecast be obtained by combining the forecast of appropriate load
component obtained from data.
3. Should simple forecasting method be used more formal mathematics procedure is
employed.
Short Term Load Forecasting: - A short term load forecasting is essentially for monitoring
and controlling power station. The hourly load forecast with load time up to one week in
advance is necessary for online salutation of scheduling problem.
A 24 hour load forecast is needed for successful operation of the power station.
Circuit Diagram:-
Fig 4.1 General load forecasting model

4. Fig 4.2 Typical daily variation of electric loads
Precautions:1. All data should be note down very carefully.
2. All reading should be read very carefully and full concentration.
3. Do not touch any live wire and equipments.
Procedure:For Long Term Load Forecasting: - One method used by many utilities:

Straight Line
Y= A+Bx

Parabola
Y= A+Bx+Cx2

‘S’ Curve
Y= A+Bx+Cx2+Dx3

Exponential
Y= CeDx

5. Where Y = Load
x = Year
A, B, C, D = Constant
For Short Term Load Forecasting: - Short term load forecasting technique generally
involves physical decomposition of load in to component. The random error can be statically
analysed to obtain a Stochastic model for error estimated.
Y (i, j) = ADP (j) + AWP (k, j) + WSC (i, j) + TR (i) + SEC (i, j)
Where Y (i, j) = load forecast for jth hour of ith day
ADP (j) = Average Daily Load Pattern at jth hour
AWP (k, j) = Average Weekly Load Increment Pattern
WSC (i, j) = Weather Sensitive Component
TR (i) = Tread Component of Load
SEC (i, j) = Stochastic Error Component
Observation Table:-
Calculation:-
Result:We have successfully studied about Methods of short term, medium term and long term load
forecasting.

6. Viva- Voice Questions:Ques. 1 what is Load Forecasting? And why we use?
Ques. 2 Write the name of techniques which is used in load forecasting.
Ques. 3 Write the name of factors which must be taken into account when load forecasting
perform.

7. Viva- Voice Answers:Ans. 1 Load forecasts have long been recognized as the initial building block for all utility
planning efforts. 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.
Ans. 2 various regression models, time series, neural networks, expert systems, fuzzy logic,
and statistical learning algorithms, are used for short-term forecasting. End-use and
econometric approach is broadly used for medium- and long-term forecasting.
Ans. 3 Time factors, weather data, and possible customers, historical load , age, the economic
and demographic data and the appliance sales data.