Abstract
Electric load forecasting is a real-life problem in industry. Electricity supplier's use forecasting models to predict the load demand of their customers to increase/decrease the power generated and to minimize the operating costs of producing electricity. This paper presents the development and the implementation of three new electricity demand-forecasting models using the adaptive neuro-fuzzy inference system (ANFIS) approach in parallel load series. The input-output data pairs used are the real-time quart-hourly metropolitan France electricity load obtained from the RTE website and forecasts are done for lead-time of a 1 h ahead. Results and forecasting performance obtained reveal the effectiveness of the third proposed approach and shows that 56 % of the forecasted loads have an APE (absolute percentage error) under 0.5, and an APE under one was achieved for about 80 % of cases. Which mean that it is possible to build a high accuracy model with less historical data using a combination of neural network and fuzzy logic.