Long Term Load Forecasting using Improved Recurrent Neural Network

Abstract

In general, electric power companies must prepare power supply capability for maximum electric load demand, because it is very difficult to store electric power at present. It takes several years and requires a great amount of money to construct power generation and transmission facilities. Therefore, it is necessary to forecast long term load demand exactly in order to plan or operate power systems efficiently. Several methods have been investigated so far for the long term load forecasting. However, because the electric loads consist of many complex factors, good forecasting has been very difficult. In this paper, we propose a long term load forecasting method using a recurrent neural network (RNN). A recurrent neural network is a mutually connected network, and has an ability of learning patterns and past records. In general, when interpolation is used for unlearned data sets, the neural network gives reasonably good outputs. However, when extrapolation is used such as in the long term load forecasting, some kind of tunings have been necessary to obtain good results. Therefore, in order to solve the problem, we propose a method in which growth rates are used as input and output data. Using the proposed method, successful results have been obtained, and comparisons have been made with the conventional methods.