Fuzzy and Neuro-Fuzzy Computing Models for Load Forecasting
P.K. Dash, A.C. Liew, S. Rahman, S. Dash
Journal of Engineering Applications of Artificial Intelligence, vol. 8, no. 4, pp.423-433.
Two new computing models, namely a fuzzy expert system and a hybrid neural network-fuzzy expert system for time series forecasting of electric load, are presented in this paper. The fuzzy-logic-based expert system utilizes the historical relationship between load and dry-bulb temperature, and predicts electric loads fairly accurately, 1â€“24 h ahead. In the case of the hybrid neural network-fuzzy expert system, the input vector consists of the membership values of the linguistic properties of the past load and weather parameters, and the output vector is defined in terms of the fuzzy class membership values of the forecasted load. Linear and nonlinear adaptive fuzzy correction schemes are then used to augment the output from the neuro-fuzzy computing model to yield the final forecast. Extensive studies have been performed for load prediction for all seasons, and the results for a typical winter day are given to confirm the effectiveness of these models.