Combined empirical mode decomposition and dynamic regression model for forecasting electricity load demand

Electricity load demand forecasting is an important element in the electric power industry for energy system planning and operation. The forecast accuracy is the main characteristic in the forecasting process. Hence, in an attempt to achieve a good forecast, combined methods of empirical mode decomp...

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Main Author:	Akrom, Nuramirah
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/53549/1/NuramirahAkromMFS2015.pdf
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id	my-utm-ep.53549
record_format	uketd_dc
spelling	my-utm-ep.535492020-07-20T01:45:07Z Combined empirical mode decomposition and dynamic regression model for forecasting electricity load demand 2015-03 Akrom, Nuramirah QA Mathematics Electricity load demand forecasting is an important element in the electric power industry for energy system planning and operation. The forecast accuracy is the main characteristic in the forecasting process. Hence, in an attempt to achieve a good forecast, combined methods of empirical mode decomposition (EMD) and dynamic regression (DR), known as EMD-DR is proposed. Besides, the forecast performance of the combined model EMD and DR is compared with a single DR model. EMD is a powerful analysis technique for detecting non-stationary and nonlinear signal, while DR is a method that involves lagged external variables. The data used in this study are retrieved from half-hourly electricity demand (kW) and reactive power (var), whereby the reactive power data acts as exogenous variable for the DR method. The investigation is conducted using Statistical Analysis Software (SAS) for DR method and Matlab software for EMD. The findings reveal that the combined method, EMD-DR, give mean absolute percentage error (MAPE) 0.7237%, whereas for the DR method, 0.8074% is obtained, which suggests percentage improvement of 10.37%. 2015-03 Thesis http://eprints.utm.my/id/eprint/53549/ http://eprints.utm.my/id/eprint/53549/1/NuramirahAkromMFS2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:84487 masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Akrom, Nuramirah Combined empirical mode decomposition and dynamic regression model for forecasting electricity load demand
description	Electricity load demand forecasting is an important element in the electric power industry for energy system planning and operation. The forecast accuracy is the main characteristic in the forecasting process. Hence, in an attempt to achieve a good forecast, combined methods of empirical mode decomposition (EMD) and dynamic regression (DR), known as EMD-DR is proposed. Besides, the forecast performance of the combined model EMD and DR is compared with a single DR model. EMD is a powerful analysis technique for detecting non-stationary and nonlinear signal, while DR is a method that involves lagged external variables. The data used in this study are retrieved from half-hourly electricity demand (kW) and reactive power (var), whereby the reactive power data acts as exogenous variable for the DR method. The investigation is conducted using Statistical Analysis Software (SAS) for DR method and Matlab software for EMD. The findings reveal that the combined method, EMD-DR, give mean absolute percentage error (MAPE) 0.7237%, whereas for the DR method, 0.8074% is obtained, which suggests percentage improvement of 10.37%.
format	Thesis
qualification_level	Master's degree
author	Akrom, Nuramirah
author_facet	Akrom, Nuramirah
author_sort	Akrom, Nuramirah
title	Combined empirical mode decomposition and dynamic regression model for forecasting electricity load demand
title_short	Combined empirical mode decomposition and dynamic regression model for forecasting electricity load demand
title_full	Combined empirical mode decomposition and dynamic regression model for forecasting electricity load demand
title_fullStr	Combined empirical mode decomposition and dynamic regression model for forecasting electricity load demand
title_full_unstemmed	Combined empirical mode decomposition and dynamic regression model for forecasting electricity load demand
title_sort	combined empirical mode decomposition and dynamic regression model for forecasting electricity load demand
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2015
url	http://eprints.utm.my/id/eprint/53549/1/NuramirahAkromMFS2015.pdf
_version_	1747817579045453824

Combined empirical mode decomposition and dynamic regression model for forecasting electricity load demand

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