A neural fuzzy network with optimal number of rules for short-term load forecasting in an intelligent home

Abstract

In this paper, a short-term home daily load forecasting realized by a neural fuzzy network (NFN) and an improved genetic algorithm (GA) is proposed. It can forecast the daily load accurately with respect to different day types and weather information. It will also be shown that the improved GA performs better than the traditional GA on some benchmark test functions. By introducing switches in the links of the neural fuzzy network, the optimal network structure can be found by the improved GA. The membership functions and the number of rules of the neural fuzzy network can be generated automatically. Simulation results for a short-term daily load forecasting in an intelligent home will be given.