Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions...

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Autores principales:	Joanna Kajewska-Szkudlarek, Jan Bylicki, Justyna Stańczyk, Paweł Licznar
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	outdoor temperature forecasting HVAC systems Akaike information criterion neural networks predictor selection Technology T
Acceso en línea:	https://doaj.org/article/63341b1fc5e74f61ad7ecb6d7de44850
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spelling	oai:doaj.org-article:63341b1fc5e74f61ad7ecb6d7de448502021-11-25T17:26:11ZNeural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems10.3390/en142275121996-1073https://doaj.org/article/63341b1fc5e74f61ad7ecb6d7de448502021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7512https://doaj.org/toc/1996-1073An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling.Joanna Kajewska-SzkudlarekJan BylickiJustyna StańczykPaweł LicznarMDPI AGarticleoutdoor temperature forecastingHVAC systemsAkaike information criterionneural networkspredictor selectionTechnologyTENEnergies, Vol 14, Iss 7512, p 7512 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	outdoor temperature forecasting HVAC systems Akaike information criterion neural networks predictor selection Technology T
spellingShingle	outdoor temperature forecasting HVAC systems Akaike information criterion neural networks predictor selection Technology T Joanna Kajewska-Szkudlarek Jan Bylicki Justyna Stańczyk Paweł Licznar Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems
description	An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling.
format	article
author	Joanna Kajewska-Szkudlarek Jan Bylicki Justyna Stańczyk Paweł Licznar
author_facet	Joanna Kajewska-Szkudlarek Jan Bylicki Justyna Stańczyk Paweł Licznar
author_sort	Joanna Kajewska-Szkudlarek
title	Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems
title_short	Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems
title_full	Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems
title_fullStr	Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems
title_full_unstemmed	Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems
title_sort	neural approach in short-term outdoor temperature prediction for application in hvac systems
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/63341b1fc5e74f61ad7ecb6d7de44850
work_keys_str_mv	AT joannakajewskaszkudlarek neuralapproachinshorttermoutdoortemperaturepredictionforapplicationinhvacsystems AT janbylicki neuralapproachinshorttermoutdoortemperaturepredictionforapplicationinhvacsystems AT justynastanczyk neuralapproachinshorttermoutdoortemperaturepredictionforapplicationinhvacsystems AT pawełlicznar neuralapproachinshorttermoutdoortemperaturepredictionforapplicationinhvacsystems
_version_	1718412353724219392

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

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