EMD-DR models for forecasting electricity load demand
Forecasting electricity demand is a vital process since electricity is a hard-to-store resource. To accurately forecast electricity demand, this paper proposes a novel method combining Empirical Mode Decomposition (EMD) and Dynamic Regression namely EMD-DR method. EMD is a technique for detecting no...
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2016
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my.utm.713902017-11-20T08:46:14Z http://eprints.utm.my/id/eprint/71390/ EMD-DR models for forecasting electricity load demand Akrom, Nuramirah Ismail, Zuhaimy QA Mathematics Forecasting electricity demand is a vital process since electricity is a hard-to-store resource. To accurately forecast electricity demand, this paper proposes a novel method combining Empirical Mode Decomposition (EMD) and Dynamic Regression namely EMD-DR method. EMD is a technique for detecting non-stationary and nonlinear signal, while Dynamic Regression approach is a method that involves lagged external variables. The EMD-DR method was applied to a half-hourly of electricity demand (kW) and reactive power (var) of Malaysia; where the reactive power data act as exogenous variable for Dynamic Regression method. This paper demonstrates that the proposed EMD-DR model provides a better forecast compared to a single Dynamic Regression model. Hikari Ltd. 2016 Article PeerReviewed Akrom, Nuramirah and Ismail, Zuhaimy (2016) EMD-DR models for forecasting electricity load demand. Contemporary Engineering Sciences, 9 (13-16). pp. 763-780. ISSN 1313-6569 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992190539&doi=10.12988%2fces.2016.6430&partnerID=40&md5=4b276250f6510f341fd52f9703c940ae |
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QA Mathematics Akrom, Nuramirah Ismail, Zuhaimy EMD-DR models for forecasting electricity load demand |
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Forecasting electricity demand is a vital process since electricity is a hard-to-store resource. To accurately forecast electricity demand, this paper proposes a novel method combining Empirical Mode Decomposition (EMD) and Dynamic Regression namely EMD-DR method. EMD is a technique for detecting non-stationary and nonlinear signal, while Dynamic Regression approach is a method that involves lagged external variables. The EMD-DR method was applied to a half-hourly of electricity demand (kW) and reactive power (var) of Malaysia; where the reactive power data act as exogenous variable for Dynamic Regression method. This paper demonstrates that the proposed EMD-DR model provides a better forecast compared to a single Dynamic Regression model. |
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Article |
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Akrom, Nuramirah Ismail, Zuhaimy |
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Akrom, Nuramirah Ismail, Zuhaimy |
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Akrom, Nuramirah |
title |
EMD-DR models for forecasting electricity load demand |
title_short |
EMD-DR models for forecasting electricity load demand |
title_full |
EMD-DR models for forecasting electricity load demand |
title_fullStr |
EMD-DR models for forecasting electricity load demand |
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EMD-DR models for forecasting electricity load demand |
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emd-dr models for forecasting electricity load demand |
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Hikari Ltd. |
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2016 |
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http://eprints.utm.my/id/eprint/71390/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992190539&doi=10.12988%2fces.2016.6430&partnerID=40&md5=4b276250f6510f341fd52f9703c940ae |
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