11-04-2011, 11:07 AM
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ABSTRACT:
This project presents the approach to the short term load forecasting using Artificial Neural Network (ANN) and Fuzzy Logic. Artificial Neural Networks (AAN) have recently been receiving considerable attention and a large number of publications concerning ANN-based short-term load forecasting (STLF) have appeared in the literature. Along with this Fuzzy Logic , also a part of Artificial Intelligence, is known to result in accurate predictions. In this paper, a model using Fuzzy Logic with the help of an ANN system is used to predict the load. There are a number of factors which affect the load, among which, temperature plays a vital role. Here, we have analyzed the forecasting of load with an added parameter of temperature.
Introduction
1.1. Back Ground:
Load forecasting is the one of the central function in power system operations. The motivation for accurate forecasts lies in the nature of electricity as a commodity and trading article. Electricity cannot be stored, which means that for an electric utility the estimate of the future demand necessary in managing the production and purchasing in an economically reasonable way.
Load forecasting methods can be divided into very short term, mid and long term models according to the time span. In very short term load forecasting the prediction time can be as short as a few minutes, while in the long term forecasting it is from a few years up to several decades. This work concentrates on short term load forecasting, where the prediction time varies between a few hours and about one week.
Short term load forecasting (STLF) has been lately a very commonly addressed problem in power system literature. One reason is that recent scientific innovations have brought in new approaches to solve the problem. The development in computer technology has broadened possibilities for these and other methods working and a real time environment. Another reason may be that there is an international movement towards greater competition in electricity markets.
Even if many forecasting procedures have been tested and proven successful, none has achieved a strong stature as a generally applied method. A reason is that the circumstances and requirements of a particular situation have a significant influence on choosing the appropriate model. The results presented in the literature are usually not directly comparable to each other.
A majority of the recently reported approaches are based on Artificial Neural Networks and Fuzzy logic (FL) systems have each yielded very encouraging results in solving the problem of Short term load forecasting (STLF). Model combinations such as fuzzy pre-processing of neural network inputs and fuzzy post processing of neural networks outputs have yielded advances in reducing the forecasting error.
This project describes the development of a FL based model for STLF. The developed model will provide a daily profile for 24-hour a head load forecast for normal week days and holidays.
1.2. IMPORTANCE OF THE LOAD FORECASTING:
Load forecasting has always been important for planning and operational decision conducted 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. In the deregulated economy, decisions on capital expenditures based on long-term forecasting are also more important than in a non-deregulated economy when rate increases could be justified by capital expenditure projects.
1.3. THE PURPOSE OF WORK:
This project studies the applicability of Fuzzy Logic model with a base of Artificial Neural model on Short Term Load Forecasting. This model forecasts the load for one whole day at a time. Testing is carried out on the real load data of Delhi electric utility.
As there is need to forecast the load accurately at all spans, another goal is to study the performance of the models for different lead times. Intuitively, it seems possible that different models should be preferred for different time spans even within the short term forecasting range.
The work provides the basis for an automatic forecasting application to be used in a real-time environment.
There are some properties, which are considered important:
- The model should be automatic and able to adapt quickly to changes in the load behavior.
- The model is intended for use in many different cases. This means that generality is desired.
- Updating the forecast with new available data should be possible. The hours closest to the forecasting time should always be forecast as accurately as possible.
- This model should be reliable. Even exceptional circumstances must not give rise to unreasonable forecasts.
- Outdoor temperature should be taken care of.
- The model should be easily attachable to an energy management Different weather conditions typical in Delhi, especially, large variation of system.
- 1.4. THE STRUCTURE OF THE WORK:
In first chapter a brief survey of previous work done on the STLF is discussed. It concentrates on the subject of load forecasting in general, and needs and uses of the Short Term Load Forecasting.
In the second chapter, first, the properties of the load curve of electric utility and different factors affecting the load are discussed. Then possible approaches to the problem are considered. The most popular conventional methods are briefly introduced.
In third chapter discusses Fuzzy Logic (FL) models and their use in load forecasting. First, a short general introduction to FL is given. Then, the most popular network type, the Multi-Layer Perceptron network (MLP) is described. The basic idea in applying MLP based methods to the problem at hand is given. A literature survey on FL Short Term Load Forecasting models is carried out at the end of the chapter. Finally, a description of the FL based hourly forecasting for one week in different seasons by using different models is given. And it Created by MAHEENis also explained the effect of the temperature on the load forecasting.
In fourth chapter, FL based short term load forecasting is discussed, and also discussed about the inputs to be chosen to FL for getting better forecasting results.
In sixth chapter, conclusions and suggestions for further research are given.
