Real-Coded GA for Parameter Optimization in Short-Term Load Forecasting

Authors:
Hara P. Satpathy
Affiliations:
Barry University, Miami Shores 33161
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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Abstract

This work explores the possibility of real-coded genetic optimization of the parameters of a Fuzzy Neural Network (FNN) for short-term electric load forecasting. A new approach is proposed to optimize the connecting weights and the structure of membership function. The adjustable parameters of the network such as connecting weights and rule sets of the network are viewed as constraints that we impose on GA in the learning process. Instead of working on the conventional bit by bit operation, both the crossover and mutation operators are real-value handled. The effectiveness of the proposed algorithm is demonstrated by comparison to a non-GA based forecasting approach. The algorithm is comprehensively tested with actual load data of an electric utility and the Mean Absolute of Percentage of Error (MAPE) is below 2.0% in most.