Short load forecasting by using a hybrid model of adaptive Neuro-Fuzzy system for electric load: article / Muhammad Aidil Adha Aziz

Load forecasting is crucially necessary for the electric for any power system and real- life difficulty in industry in the deregulated economy. It has various applications including energy purchasing and generation, contract evaluation, load switching, infrastructure development and to forecast the load demand from the customers by rising or declining the power generated and to lessen the operating costs of producing electricity. Besides, the conventional traditional models, some models based on artificial intelligence have been purposed in the literature, specifically, neural network for their good performance. Other non-parametric approaches of artificial intelligence have also been applied. However, all these models are imprecise when used in real time operation. The purpose of this research is to present an electric system load forecasting model using an adaptive NeuroFuzzy interface system (ANFIS) and discuss in detail how ANFIS is effectively applied to weekly, short term load forecasting with respect to different day types. The outcome and forecasting performance obtained reveal the effectiveness of the proposed approach and shows that it has potential to build a high accurateness model with less historical data using a hybrid of neural network and fuzzy logic which can be used in real time.