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Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm

Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm
Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm
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.
ANFIS, ARIMA, AdaBoost, Energy forecasting, Ensemble algorithm
0142-0615
92-104
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Sadegh, S. Saeedeh
537b9817-a8ff-486c-af5c-511911393046
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Sadegh, S. Saeedeh
537b9817-a8ff-486c-af5c-511911393046

Barak, Sasan and Sadegh, S. Saeedeh (2016) Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm. International Journal of Electrical Power & Energy Systems, 82, 92-104. (doi:10.1016/j.ijepes.2016.03.012).

Record type: Article

Abstract

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.

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More information

Accepted/In Press date: 9 March 2016
e-pub ahead of print date: 23 March 2016
Published date: 1 November 2016
Keywords: ANFIS, ARIMA, AdaBoost, Energy forecasting, Ensemble algorithm

Identifiers

Local EPrints ID: 434857
URI: https://eprints.soton.ac.uk/id/eprint/434857
ISSN: 0142-0615
PURE UUID: 47678f86-0fda-4938-a736-d27af6ff036b
ORCID for Sasan Barak: ORCID iD orcid.org/0000-0001-7715-9958

Catalogue record

Date deposited: 11 Oct 2019 16:30
Last modified: 10 Dec 2019 01:20

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Contributors

Author: Sasan Barak ORCID iD
Author: S. Saeedeh Sadegh

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