Forecasting and Modelling Electricity Demand Using Anfis Predictor

Abstract

Problem statement: Load forecasting plays an important task in power system planning, operation and control. It has received an increasing attention over the years by academic researchers and practitioners. Control, security assessment, optimum planning of power production required a precise short term load forecasting. Approach: This study tries to combine neural network and fuzzy logic for next week electric load forecasting. The suitability of the proposed approach is illustrated through an application to electric load consumption data in 2010 downloaded from the RTE France website. Results: The study presents the results and evaluates them. Corresponding code was developed and used to forecast the next week load in a practical power system and the final forecasting result is perfect and consistent. Conclusion: The ANFIS system provides a useful and suitable tool especially for the load forecasting. The forecasting accuracy is high.

Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System

“…As regards the electricity domain, ANFIS model was applied to forecast annual regional load in Taiwan [85] and annual demand in Turkey [86], showing in both cases that the results are good and the ANFIS model performed better than regression, neural network and fuzzy hybrid systems. ANFIS was also used in [87] for short-term electricity demand forecasting, using weekly electricity load data, as well as in [88], to estimate possible improvement of electricity consumption. Also, for electricity load forecasting, ANFIS was used in [89] to highlight its superiority to the ANN model, while it was furthermore applied in the field of transportation, forecasting the corresponding energy demand for the years 2010 to 2030, in the country of Jordan, revealing the efficiency of the examined model.…”