Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction

Modern computing resources, including machine learning-based techniques, are used to maintain stability between the demand and supply of electricity. Machine learning is widely used for the prediction of energy consumption. The researchers present several artificial intelligence and machine learning...

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Autores principales:	Prince Waqas Khan, Yongjun Kim, Yung-Cheol Byun, Sang-Joon Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning energy consumption energy prediction hybrid model error curve learning Technology T
Acceso en línea:	https://doaj.org/article/7f03c1eb0d12479085f027c6a73f6115
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spelling	oai:doaj.org-article:7f03c1eb0d12479085f027c6a73f61152021-11-11T15:56:50ZInfluencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction10.3390/en142171671996-1073https://doaj.org/article/7f03c1eb0d12479085f027c6a73f61152021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7167https://doaj.org/toc/1996-1073Modern computing resources, including machine learning-based techniques, are used to maintain stability between the demand and supply of electricity. Machine learning is widely used for the prediction of energy consumption. The researchers present several artificial intelligence and machine learning-based methods to improve the prediction accuracy of energy consumption. However, the discrepancy between actual energy consumption and predicted energy consumption is still challenging. Various factors, including changes in weather, holidays, and weekends, affect prediction accuracy. This article analyses the overall prediction using error curve learning and a hybrid model. Actual energy consumption data of Jeju island, South Korea, has been used for experimental purposes. We have used a hybrid ML model consisting of Catboost, Xgboost, and Multi-layer perceptron for the prediction. Then we analyze the factors that affect the week-ahead (WA) and 48 h prediction results. Mean error on weekdays is recorded as 2.78%, for weekends 2.79%, and for special days it is recorded as 4.28%. We took into consideration significant predicting errors and looked into the reasons behind those errors. Furthermore, we analyzed whether factors, such as a sudden change in temperature and typhoons, had an effect on energy consumption. Finally, the authors have considered the other factors, such as public holidays and weekends, to analyze the significant errors in the prediction. This study can be helpful for policymakers to make policies according to the error-causing factors.Prince Waqas KhanYongjun KimYung-Cheol ByunSang-Joon LeeMDPI AGarticlemachine learningenergy consumptionenergy predictionhybrid modelerror curve learningTechnologyTENEnergies, Vol 14, Iss 7167, p 7167 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning energy consumption energy prediction hybrid model error curve learning Technology T
spellingShingle	machine learning energy consumption energy prediction hybrid model error curve learning Technology T Prince Waqas Khan Yongjun Kim Yung-Cheol Byun Sang-Joon Lee Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction
description	Modern computing resources, including machine learning-based techniques, are used to maintain stability between the demand and supply of electricity. Machine learning is widely used for the prediction of energy consumption. The researchers present several artificial intelligence and machine learning-based methods to improve the prediction accuracy of energy consumption. However, the discrepancy between actual energy consumption and predicted energy consumption is still challenging. Various factors, including changes in weather, holidays, and weekends, affect prediction accuracy. This article analyses the overall prediction using error curve learning and a hybrid model. Actual energy consumption data of Jeju island, South Korea, has been used for experimental purposes. We have used a hybrid ML model consisting of Catboost, Xgboost, and Multi-layer perceptron for the prediction. Then we analyze the factors that affect the week-ahead (WA) and 48 h prediction results. Mean error on weekdays is recorded as 2.78%, for weekends 2.79%, and for special days it is recorded as 4.28%. We took into consideration significant predicting errors and looked into the reasons behind those errors. Furthermore, we analyzed whether factors, such as a sudden change in temperature and typhoons, had an effect on energy consumption. Finally, the authors have considered the other factors, such as public holidays and weekends, to analyze the significant errors in the prediction. This study can be helpful for policymakers to make policies according to the error-causing factors.
format	article
author	Prince Waqas Khan Yongjun Kim Yung-Cheol Byun Sang-Joon Lee
author_facet	Prince Waqas Khan Yongjun Kim Yung-Cheol Byun Sang-Joon Lee
author_sort	Prince Waqas Khan
title	Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction
title_short	Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction
title_full	Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction
title_fullStr	Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction
title_full_unstemmed	Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction
title_sort	influencing factors evaluation of machine learning-based energy consumption prediction
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7f03c1eb0d12479085f027c6a73f6115
work_keys_str_mv	AT princewaqaskhan influencingfactorsevaluationofmachinelearningbasedenergyconsumptionprediction AT yongjunkim influencingfactorsevaluationofmachinelearningbasedenergyconsumptionprediction AT yungcheolbyun influencingfactorsevaluationofmachinelearningbasedenergyconsumptionprediction AT sangjoonlee influencingfactorsevaluationofmachinelearningbasedenergyconsumptionprediction
_version_	1718432544309903360

Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction

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