Multiple linear regression and neural network for electric load forecasting

Starting from conventional models, researchers have begun to develop advanced techniques. One recent technique is the hybrid model, which improves upon the time series forecast. In this study, a hybrid model combining the multiple linear regression (MLR) model and neural network (NN) model has been...

Full description

Saved in:

Bibliographic Details
Main Author:	Kamisan, Nur Arina Bazilah
Format:	Thesis
Language:	English
Published:	2017
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/79166/1/NurArinaBazilahPFS2017.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.79166
record_format	uketd_dc
spelling	my-utm-ep.791662018-10-04T03:28:20Z Multiple linear regression and neural network for electric load forecasting 2017 Kamisan, Nur Arina Bazilah QA Mathematics Starting from conventional models, researchers have begun to develop advanced techniques. One recent technique is the hybrid model, which improves upon the time series forecast. In this study, a hybrid model combining the multiple linear regression (MLR) model and neural network (NN) model has been developed to enhance the forecast of Malaysian short term load. Considering the data consisted of linear and nonlinear parts, it is first forecasted using the MLR model. The residuals obtained from the in-sample forecast are then forecasted using the NN model. This model has improved the forecast, although at certain hours, neural network model gives better performance. To determine the performance of the models, three performance indicators are used: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). To assist in error measurements, we also developed a fractional residual plot to observe goodness-of-fit. A graphical plot could help an analyst see the goodness of the analysis for each of the individual data. Compared to the regular residual plot, this plot provides more information and can be used as a benchmark tool. This study also includes the missing values problem as one of the objectives. In load data, the missing problem always occurs in a set of data. Since it has a seasonal pattern according to days, most of the time, the load usage for the next day is predictable. For this reason, a new model has been developed based on these characteristics. Three imputations are tested with this method: mean (DCM1), mean + standard deviation (DCM2) and third quartile value (DCM3). The data is divided into three parts which are at the front, middle and at the end of the data with 5%, 15%, and 25% of missing values. The results of RMSE show that the proposed techniques, particularly DCM1 and DCM3, are superior to other complex methods when dealing with missing values. 2017 Thesis http://eprints.utm.my/id/eprint/79166/ http://eprints.utm.my/id/eprint/79166/1/NurArinaBazilahPFS2017.pdf application/pdf en public phd doctoral 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 Kamisan, Nur Arina Bazilah Multiple linear regression and neural network for electric load forecasting
description	Starting from conventional models, researchers have begun to develop advanced techniques. One recent technique is the hybrid model, which improves upon the time series forecast. In this study, a hybrid model combining the multiple linear regression (MLR) model and neural network (NN) model has been developed to enhance the forecast of Malaysian short term load. Considering the data consisted of linear and nonlinear parts, it is first forecasted using the MLR model. The residuals obtained from the in-sample forecast are then forecasted using the NN model. This model has improved the forecast, although at certain hours, neural network model gives better performance. To determine the performance of the models, three performance indicators are used: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). To assist in error measurements, we also developed a fractional residual plot to observe goodness-of-fit. A graphical plot could help an analyst see the goodness of the analysis for each of the individual data. Compared to the regular residual plot, this plot provides more information and can be used as a benchmark tool. This study also includes the missing values problem as one of the objectives. In load data, the missing problem always occurs in a set of data. Since it has a seasonal pattern according to days, most of the time, the load usage for the next day is predictable. For this reason, a new model has been developed based on these characteristics. Three imputations are tested with this method: mean (DCM1), mean + standard deviation (DCM2) and third quartile value (DCM3). The data is divided into three parts which are at the front, middle and at the end of the data with 5%, 15%, and 25% of missing values. The results of RMSE show that the proposed techniques, particularly DCM1 and DCM3, are superior to other complex methods when dealing with missing values.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Kamisan, Nur Arina Bazilah
author_facet	Kamisan, Nur Arina Bazilah
author_sort	Kamisan, Nur Arina Bazilah
title	Multiple linear regression and neural network for electric load forecasting
title_short	Multiple linear regression and neural network for electric load forecasting
title_full	Multiple linear regression and neural network for electric load forecasting
title_fullStr	Multiple linear regression and neural network for electric load forecasting
title_full_unstemmed	Multiple linear regression and neural network for electric load forecasting
title_sort	multiple linear regression and neural network for electric load forecasting
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2017
url	http://eprints.utm.my/id/eprint/79166/1/NurArinaBazilahPFS2017.pdf
_version_	1747818162374574080

Multiple linear regression and neural network for electric load forecasting

Similar Items