Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F16%3A86100110" target="_blank" >RIV/61989100:27510/16:86100110 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.ijepes.2016.03.012" target="_blank" >http://dx.doi.org/10.1016/j.ijepes.2016.03.012</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ijepes.2016.03.012" target="_blank" >10.1016/j.ijepes.2016.03.012</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm

Popis výsledku v původním jazyce

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. (C) 2016 Elsevier Ltd. All rights reserved.

Název v anglickém jazyce

Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm

Popis výsledku anglicky

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. (C) 2016 Elsevier Ltd. All rights reserved.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

—

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Electrical Power &amp; Energy Systems

ISSN

0142-0615

e-ISSN

—

Svazek periodika

82

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

92-104

Kód UT WoS článku

000378449700011

EID výsledku v databázi Scopus

2-s2.0-84961825685