An Optimal Design Methodology of Adaptive Neuro-Fuzzy Inference System for Energy Load Forecasting - Hail city case study (Saudi Arabia)

In this paper, it is shown that accurate load forecasts are vital for short, medium and long-term operations. The energy load forecast has its impact on different outcomes and decisions for power generation companies. It also has its influence on electricity market prices. The purpose of this research is to develop an energy load forecasting model to predict future electricity loads for energy load management. The forecasting model is based on a straightforward sequential methodology by implementing subtractive clustering algorithm, fuzzy C-means clustering algorithm and eventually an adaptive Neuro-Fuzzy inference system architecture for generating the best fuzzy inference system using historical energy load data. In addition, the influence of different weather factors on energy loads such as dry-bulb temperature is counted in.