Determining the model for short-term load forecasting using fuzzy logic and ANFIS

Abstract

Short-term load forecasting (STLF) usually begins by grouping data according to various criteria, most often by days of the week. Then, based on the obtained segments, independent models are created. Each model’s prediction uses only one segment of the data. This paper proposes a new approach to model formation based on the correlation between the forecasted day and previous days. The proposed approach is compared with the usual approach where data segments are obtained by grouping according to days of the week. The models were created using fuzzy logic and ANFIS. The mean absolute percentage errors of the new approach and the usual approach using ANFIS in terms of prediction accuracy are obtained as 2.89 and 4.15, respectively. The mean absolute percentage errors for the new approach and the usual approach are 3.39 and 4.78, respectively, when fuzzy logic is used. The results showed that when the proposed method is used, forecasts for the day ahead are much more accurate in both cases.