A fuzzy logic controller based mid-term load forecasting with renewable penetration in Assam, India

An accurate mid-term load forecasting (MTLF) tool is an essential part of power systems planning and sustainable development. In order to compensate the extra uncertainties, the power systems with high renewable penetration need even more accurate MTLF tool. The electric load demand is highly prejudiced by the thermal inertia due to the local climatic factors. Therefore, the accuracy of an MTLF method is highly dependent on the incorporated climatic factors. This paper proposes a fuzzy logic comptroller based MTLF method with renewable penetration. In order to achieve a higher degree of forecasting accuracy proposed method incorporated several climatic factors in the forecasting process. The study is done in Assam, a state of India and the proposed method is utilized to forecast the daily average load demand for one month. The forecasting accuracy of the proposed method is compared with one of most commonly used tool for MTLF called artificial neural network (ANN). The empirical results affirm the superiority of the proposed method over the ANN.