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- 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.

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