Application of Artificial Neural Network for Short Term Load Forecasting in Electrical Power System

Application of Artificial Neural Network for Short Term Load Forecasting in Electrical Power System

ABSTRACT:

This paper presents a method for short term load forecasting in eectric power system using artificial neural network. A multilayered feed forward network with back propagation learning algorithm is used because its good generalising property. The input to the neural network is in terms of past load data which was heuristically chosen such that they reflect the trend, load shape as well as some influence of weather. The weather data is not used to train the metwork. The network is trained to predict one hour ahead load forecasting. The generalisation capability of the neural network is also studied. Simulation results using the system data are presented.

Introduction:

Short term load forecasting is an essential tool in operation and planning of the power system. It helps in coordinating the generation and area interchange to meet the load demand. It also helps in security assessment, dynamic state estimation, load management and other related functions. In the last few decades, various methods for short term load forecasting have been proposed. The methods vary from simple regression and extrapolation of fading memory Kalman filter and knowledge based systems.

Among the various methods available in the literature, most can be classified into two categories. In the first category are the methods, which rely solely on the past data and fit the load pattern as a time series. In the second category are the methods, which give emphasis to the weather variables, ie, temperature, humidity, lightintensity, etc, and find a functional relationship between these variables and the load demand.

Recently, Artificial Neural Networks (ANN) has been used for short term load forecasting. Both time series models and weather dependent models have been used in ANN based short tem load forecasting. In this paper, a short-term load forecasting method using the ANN is proposed. A multilayered feed forward (MLFF) neural network with back propagation learning algorithm has been used because of its simplicity and good generalization property. The input, to the neural network is based only on past load data and are heuristically chosen in such a manner that they inherently reflect all the major components, such as, trend, type of day, load shape as well as weather which influence the system load.

The main contributions of this paper are: (i) Heuristic choice of a small set of input which inherently represents the major components of the load pattern (ii) introduction of a stopping criteria during learning phase to avoid over fitting of the network to learning examples, and (iii) A detailed analysis of the generalisation properties like interpolation/extrapolation ability of the ANN, working life of a trained network, ie, useful period of a network after which a retaining is required etc.


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