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A novel short-term load forecasting approach using Adaptive Neuro-Fuzzy Inference System

By developments on smart grid technologies, the management of the grid issue get more importance. Load forecasting is one of the most important topics for managing the grid. In this study, it is aimed to develop a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) based load forecasting approach for small regions having less consumption. For this aim Çay vocational high school campus area of Afyon Kocatepe University is considered. Measured and recorded hourly load demand of the campus is predicted using proposed ANFIS based model. In proposed forecasting model, only consumption parameter is required. The other parameters are calculated from this parameter. In this scope, first order derivative of the consumption, actual consumption, month and hour parameters are used as the inputs of ANFIS model to forecast next hour load demand. The experimental results show that the proposed approach is well in load forecasting for small regions.