Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm

• Published in: International journal of electrical power & energy systems Vol. 82; pp. 92 - 104
• Main Authors: Barak, Sasan, Sadegh, S. Saeedeh
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2016
• Subjects: AdaBoost; Adaptive systems; Algorithms; ANFIS; ARIMA; Artificial neural networks; Demand; Energy consumption; Energy forecasting; Ensemble algorithm; Forecasting; Fuzzy logic; Mathematical models; Ensemble algorithm; AdaBoost; ANFIS; ARIMA; Energy forecasting
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2016.03.012

•Developing a hybrid ARIMA–ANFIS algorithm based on three different patterns.•Using diversification method to deal with data insufficiency.•Finally, comparing all patterns with different prediction models. Energy consumption is on the rise in developing economies. In order to improve present and future energy supplies, forecasting energy demands is essential. However, lack of accurate and comprehensive data set to predict the future demand is one of big problems in these countries. Therefore, using ensemble hybrid forecasting models that can deal with shortage of data set could be a suitable solution. In this paper, the annual energy consumption in Iran is forecasted using 3 patterns of ARIMA–ANFIS model. In the first pattern, ARIMA (Auto Regressive Integrated Moving Average) model is implemented on 4 input features, where its nonlinear residuals are forecasted by 6 different ANFIS (Adaptive Neuro Fuzzy Inference System) structures including grid partitioning, sub clustering, and fuzzy c means clustering (each with 2 training algorithms). In the second pattern, the forecasting of ARIMA in addition to 4 input features is assumed as input variables for ANFIS prediction. Therefore, four mentioned inputs beside ARIMA’s output are used in energy prediction with 6 different ANFIS structures. In the third pattern, due to dealing with data insufficiency, the second pattern is applied with AdaBoost (Adaptive Boosting) data diversification model and a novel ensemble methodology is presented. The results indicate that proposed hybrid patterns improve the accuracy of single ARIMA and ANFIS models in forecasting energy consumption, though third pattern, used diversification model, acts better than others and model’s MSE criterion was decreased to 0.026% from 0.058% of second hybrid pattern. Finally, a comprehensive comparison between other hybrid prediction models is done.