Prediction of monthly electric energy consumption using pattern-based fuzzy nearest neighbour regression

Abstract

Abstract. Electricity demand forecasting is of important role in power system planning and operation. In this work, fuzzy nearest neighbour regression has been utilised to estimate monthly electricity demands. The forecasting model was based on the pre-processed energy consumption time series, where input and output variables were defined as patterns representing unified fragments of the time series. Relationships between inputs and outputs, which were simplified due to patterns, were modelled using nonparametric regression with weighting function defined as a fuzzy membership of learning points to the neighbourhood of a query point. In an experimental part of the work the model was evaluated using real-world data. The results are encouraging and show high performances of the model and its competitiveness compared to other forecasting models.

Energy Forecasting: A Comprehensive Review of Techniques and Technologies

“…MTF for power demand is a thoroughly studied topic. Numerous approaches have been taken to resolve this problem, including similarity-based approaches [56][57][58] and classical/statistical methods [59,60]. Initially, conventional methods were used to forecast the amount of electricity consumed.…”

Study of Structural Relationships and Models of Information Support for Energy Life Cycle Management in Microgrid

“…The advantages of this method include the ability to replace large sets of forecasting data with sets of fuzzy inference, the ability to formalize the experience of experts on energy fluctuations, the versatility of the method with minor modifications for forecasts of different time periods [18]. The application of this method for medium-term forecasting of electricity consumption showed high forecasting accuracy of 96-98%, which has a slight deviation from the forecast values using ARIMA models and exponential smoothing [19].…”