Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks

By virtue of the steady societal shift to the use of smart technologies built on the increasingly popular smart grid framework, we have noticed an increase in the need to analyze household electricity consumption at the individual level. In order to work efficiently, these technologies rely on load...

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Autores principales:	Kareem Al-Saudi, Viktoriya Degeler, Michel Medema
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	pattern recognition energy profiling clustering forecasting Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/516dbe9604ba49b9bbb2b45ed40f0ed0
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id	oai:doaj.org-article:516dbe9604ba49b9bbb2b45ed40f0ed0
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spelling	oai:doaj.org-article:516dbe9604ba49b9bbb2b45ed40f0ed02021-11-25T18:49:56ZEnergy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks10.3390/pr91118702227-9717https://doaj.org/article/516dbe9604ba49b9bbb2b45ed40f0ed02021-10-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/1870https://doaj.org/toc/2227-9717By virtue of the steady societal shift to the use of smart technologies built on the increasingly popular smart grid framework, we have noticed an increase in the need to analyze household electricity consumption at the individual level. In order to work efficiently, these technologies rely on load forecasting to optimize operations that are related to energy consumption (such as household appliance scheduling). This paper proposes a novel load forecasting method that utilizes a clustering step prior to the forecasting step to group together days that exhibit similar energy consumption patterns. Following that, we attempt to classify new days into pre-generated clusters by making use of the available context information (day of the week, month, predicted weather). Finally, using available historical data (with regard to energy consumption) alongside meteorological and temporal variables, we train a CNN-LSTM model on a per-cluster basis that specializes in forecasting based on the energy profiles present within each cluster. This method leads to improvements in forecasting performance (upwards of a 10% increase in mean absolute percentage error scores) and provides us with the added benefit of being able to easily highlight and extract information that allows us to identify which external variables have an effect on the energy consumption of any individual household.Kareem Al-SaudiViktoriya DegelerMichel MedemaMDPI AGarticlepattern recognitionenergy profilingclusteringforecastingChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 1870, p 1870 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pattern recognition energy profiling clustering forecasting Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	pattern recognition energy profiling clustering forecasting Chemical technology TP1-1185 Chemistry QD1-999 Kareem Al-Saudi Viktoriya Degeler Michel Medema Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks
description	By virtue of the steady societal shift to the use of smart technologies built on the increasingly popular smart grid framework, we have noticed an increase in the need to analyze household electricity consumption at the individual level. In order to work efficiently, these technologies rely on load forecasting to optimize operations that are related to energy consumption (such as household appliance scheduling). This paper proposes a novel load forecasting method that utilizes a clustering step prior to the forecasting step to group together days that exhibit similar energy consumption patterns. Following that, we attempt to classify new days into pre-generated clusters by making use of the available context information (day of the week, month, predicted weather). Finally, using available historical data (with regard to energy consumption) alongside meteorological and temporal variables, we train a CNN-LSTM model on a per-cluster basis that specializes in forecasting based on the energy profiles present within each cluster. This method leads to improvements in forecasting performance (upwards of a 10% increase in mean absolute percentage error scores) and provides us with the added benefit of being able to easily highlight and extract information that allows us to identify which external variables have an effect on the energy consumption of any individual household.
format	article
author	Kareem Al-Saudi Viktoriya Degeler Michel Medema
author_facet	Kareem Al-Saudi Viktoriya Degeler Michel Medema
author_sort	Kareem Al-Saudi
title	Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks
title_short	Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks
title_full	Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks
title_fullStr	Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks
title_full_unstemmed	Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks
title_sort	energy consumption patterns and load forecasting with profiled cnn-lstm networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/516dbe9604ba49b9bbb2b45ed40f0ed0
work_keys_str_mv	AT kareemalsaudi energyconsumptionpatternsandloadforecastingwithprofiledcnnlstmnetworks AT viktoriyadegeler energyconsumptionpatternsandloadforecastingwithprofiledcnnlstmnetworks AT michelmedema energyconsumptionpatternsandloadforecastingwithprofiledcnnlstmnetworks
_version_	1718410646766223360

Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks

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