Daily Load Forecasting Based on a Combination of Classification and Regression Tree and Deep Belief Network

The next-day load forecasting is complex due to the load pattern variations driven by external factors, such as weather and time. This study proposes a hybrid model that incorporates the Classification and Regression Tree (CART) with pruning conditions and a Deep Belief Network (DBN) to improve fore...

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Autores principales:	Pyae Pyae Phyo, Chawalit Jeenanunta
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Classification and regression tree (CART) daily load forecasting deep belief network (DBN) forecasting accuracy pruned-CART Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/8b0ff91b3ffb46669facc8a72795cf27
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id	oai:doaj.org-article:8b0ff91b3ffb46669facc8a72795cf27
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spelling	oai:doaj.org-article:8b0ff91b3ffb46669facc8a72795cf272021-11-20T00:01:34ZDaily Load Forecasting Based on a Combination of Classification and Regression Tree and Deep Belief Network2169-353610.1109/ACCESS.2021.3127211https://doaj.org/article/8b0ff91b3ffb46669facc8a72795cf272021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9611228/https://doaj.org/toc/2169-3536The next-day load forecasting is complex due to the load pattern variations driven by external factors, such as weather and time. This study proposes a hybrid model that incorporates the Classification and Regression Tree (CART) with pruning conditions and a Deep Belief Network (DBN) to improve forecasting accuracy. The CART can recognize the load patterns by classifying similar groups with low variance, thus reducing the complexity of the forecasting model. The actual 48-period load data from the Electricity Generating Authority of Thailand (EGAT) is used. The proposed model is compared with six widely used standalone forecasting benchmark models and provides better at the minimum 0.46% mean absolute percentage error. Moreover, the forecasting performance of DBN and the other four benchmark models are improved by using our hybrid approach.Pyae Pyae PhyoChawalit JeenanuntaIEEEarticleClassification and regression tree (CART)daily load forecastingdeep belief network (DBN)forecasting accuracypruned-CARTElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152226-152242 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Classification and regression tree (CART) daily load forecasting deep belief network (DBN) forecasting accuracy pruned-CART Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Classification and regression tree (CART) daily load forecasting deep belief network (DBN) forecasting accuracy pruned-CART Electrical engineering. Electronics. Nuclear engineering TK1-9971 Pyae Pyae Phyo Chawalit Jeenanunta Daily Load Forecasting Based on a Combination of Classification and Regression Tree and Deep Belief Network
description	The next-day load forecasting is complex due to the load pattern variations driven by external factors, such as weather and time. This study proposes a hybrid model that incorporates the Classification and Regression Tree (CART) with pruning conditions and a Deep Belief Network (DBN) to improve forecasting accuracy. The CART can recognize the load patterns by classifying similar groups with low variance, thus reducing the complexity of the forecasting model. The actual 48-period load data from the Electricity Generating Authority of Thailand (EGAT) is used. The proposed model is compared with six widely used standalone forecasting benchmark models and provides better at the minimum 0.46% mean absolute percentage error. Moreover, the forecasting performance of DBN and the other four benchmark models are improved by using our hybrid approach.
format	article
author	Pyae Pyae Phyo Chawalit Jeenanunta
author_facet	Pyae Pyae Phyo Chawalit Jeenanunta
author_sort	Pyae Pyae Phyo
title	Daily Load Forecasting Based on a Combination of Classification and Regression Tree and Deep Belief Network
title_short	Daily Load Forecasting Based on a Combination of Classification and Regression Tree and Deep Belief Network
title_full	Daily Load Forecasting Based on a Combination of Classification and Regression Tree and Deep Belief Network
title_fullStr	Daily Load Forecasting Based on a Combination of Classification and Regression Tree and Deep Belief Network
title_full_unstemmed	Daily Load Forecasting Based on a Combination of Classification and Regression Tree and Deep Belief Network
title_sort	daily load forecasting based on a combination of classification and regression tree and deep belief network
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/8b0ff91b3ffb46669facc8a72795cf27
work_keys_str_mv	AT pyaepyaephyo dailyloadforecastingbasedonacombinationofclassificationandregressiontreeanddeepbeliefnetwork AT chawalitjeenanunta dailyloadforecastingbasedonacombinationofclassificationandregressiontreeanddeepbeliefnetwork
_version_	1718419847051739136

Daily Load Forecasting Based on a Combination of Classification and Regression Tree and Deep Belief Network

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