Short-Term Load Forecasting With a New Nonsymmetric Penalty Function

Abstract

In this paper, the problem of short-term load forecasting is redefined and solved with a new metric, which is the extension of the conventional sum of squared error (SSE) metric. The proposed metric is a nonsymmetric penalty function with different penalties for over-forecasting and under-forecasting. Therefore, a large family of approaches that utilize gradient-based methods such as artificial neural networks with back propagation learning and regressions method with least squares estimate are not useful in this case. To solve this problem, a modified radial basis function (RBF) network, which uses the genetic algorithm to estimate the weights of the network is presented. This network has the ability to handle the new penalty function. In addition, a fuzzy inference system is combined with the modified RBF network to incorporate the impact of temperature on load. As a real case study, we tried to forecast the electric power load of Mazandaran area in Iran. The comparison between the proposed method and the well-known RBF network demonstrates the efficiency of the proposed method with the new forecasting metric.