Development of an intelligent long-term electric load forecasting system

Abstract

An essential element of electric utility resource planning is forecasting of the future load demand in the service area. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost plan. In general, resource planning is performed subject to numerous uncertainties. Expert opinion indicates that a major source of uncertainty in planning for future capacity resource needs and operation of existing generation resources is the forecasted load demand. In this paper, the development and testing of a hybrid intelligent long-term load forecasting system is presented consisting of several neural networks forecasting blocks, genetic algorithms for network architecture selection and optimization, and fuzzy rules for forecast combination. This is an application of increasingly significant importance to a deregulating electric utility industry. An overview of the current practice in long term load forecasting is presented, followed by an overview of the forecasting system design process utilized in generating the long-term load forecasts and a brief description of the key building blocks of the forecasting system. This is followed by some sample long-term forecasts performed for demonstrating the feasibility of the proposed approach.