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Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    BB - Applied statistics, operational research

  • OECD FORD branch

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

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

Others

  • Publication year

    2016

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    International Journal of Electrical Power &amp; Energy Systems

  • ISSN

    0142-0615

  • e-ISSN

  • Volume of the periodical

    82

  • Issue of the periodical within the volume

    November

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    13

  • Pages from-to

    92-104

  • UT code for WoS article

    000378449700011

  • EID of the result in the Scopus database

    2-s2.0-84961825685