Neuro-Fuzzy System for Medium-Term Electric Energy Demand Forecasting

Abstract

Medium-term electric energy demand forecasting plays an important role in power system planning and operation as well as for negotiation forward contracts. This paper proposes a solution to medium-term energy demand forecasting that covers definition of input and output variables and the forecasting model based on a neuro-fuzzy system. As predictors patterns of the yearly periods of the time series are defined, which unify input data and filter out the trend. Output variable is encoded in tree ways using coding variables describing the process. For prediction of coding variables, which are necessary for postprocessing, ARIMA and exponential smoothing models are applied. The simplified relationship between preprocessed input and output variables is modeled using Adaptive-Network-Based Fuzzy Inference System. As an illustration, we apply the proposed time series forecasting methodology to historical monthly energy demand data in four European countries and compare its performance to that of alternative models such as ARIMA, exponential smoothing and kernel regression. The results are encouraging and confirm the high accuracy of the model and its competitiveness compared to other forecasting models.