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

In this paper, a short-term home daily load forecasting system 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 is also shown that the improved GA performs better than a traditional GA on some benchmark test functions. By introducing switches in the links of the NFN, the optimal network structure can be found by the improved GA. The membership functions and the number of rules of the NFN can be generated automatically. Simulation results for a short-term daily load forecast in an intelligent home are given.