A Fuzzy Engineering Approach for Time Series Forecasting of Electric Load

Article type: Research Article

Authors: Dash, P.K. | Dash, S. | Liew, A.C. | Rahman, Saifur

Affiliations: Department of Electrical Engineering, Regional Engineering College, Rourkela, India, e-mail: srahman@rahman.energy.ee.vt.edu | Department of Electrical Engineering, National University of Singapore | Bradley Department of Electrical Engineering, Virginia Polytechnic Institute & State University

Abstract: A hybrid neural network-fully expert system is developed to forecast short-term electric load accurately. The fuzzy membership values of load and other weather variables are the inputs to the neural network and the output comprises the membership values of the predicted load. An adaptive fuzzy correction scheme is used to forecast the final load by using a fuzzy rule base and fuzzy inference mechanism. Extensive studies have been performed for all seasons, and some examples are presented in this paper, which include average, peak, and hourly load forecasts.

DOI: 10.3233/IFS-1995-3402

Journal: Journal of Intelligent and Fuzzy Systems, vol. 3, no. 4, pp. 261-271, 1995