Medium to Long-term Peak Load Forecasting for Riyadh City Using Artificial Neural Networks

Abstract

Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future loads for a relatively large lead time (months to few years) is studied here with application to Riyadh city. Such a problem typically depends on a number of factors such as temperatures, number of customers, and past loads. However, considering other factors like special events and holidays improves the forecasting results, but makes the problem more difficult to solve with classical methods. In this paper, an Artificial Neural Network (ANN) approach to the medium/long-term load forecasting problem is presented. The results reveal that accurate estimates of future loads are achieved. Keywords: Neural networks, load forecasting