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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| Main Authors: | , |
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| Format: | Article |
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Hikari Ltd.
2016
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| Online Access: | 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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| Summary: | 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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