Optimal design of adaptive neuro-fuzzy inference system using genetic algorithm for electricity demand forecasting in Iranian industry

Demand planning for industrial’s electricity consumption is an important factor to efficiently plan the generation and distribution of power utilities. However, this can only be possible if the demand is predicted accurately. Recent advancement in adaptive neuro-fuzzy inference system aimed at mapping input to output for highly non-linear processes such as energy management field, provide reliable approach to forecast energy demand. Despite the wide range of applications and flexibility of adaptive neuro-fuzzy inference system, complexity of the rule base is featured with certain limitations associated with combinatorial explosion of rules, parameters and data. This paper proposes a hybrid procedure, subtractive clustering technique coupled with genetic algorithm, to develop adaptive neuro-fuzzy inference system. Genetic algorithm finds the optimum value of cluster radius which guaranteed the minimum number of rules and error. The empirical data regarding the industrial’s electricity demand in Iran from 1967 to 2011 are investigated to demonstrate the applicability and merits of the present method. The performance of hybrid approach is found to be better than that of conventional adaptive neuro-fuzzy inference system based on gird partitioning, fuzzy c-means, and subtractive clustering in terms of both accuracy and the number of rules.