A Pedestrian Approach to Indoor Temperature Distribution Prediction of a Passive Solar Energy Efficient House

With the increase in energy consumption by buildings in keeping the indoor environment within the comfort levels and the ever increase of energy price there is need to design buildings that require minimal energy to keep the indoor environment within the comfort levels. There is need to predict the...

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Main Author: Golden Makaka
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Journal of Renewable Energy
Online Access:http://dx.doi.org/10.1155/2015/128496
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spelling doaj-58359b9e262642628c89fe978203e2ac2020-11-25T00:00:23ZengHindawi LimitedJournal of Renewable Energy2314-43862314-43942015-01-01201510.1155/2015/128496128496A Pedestrian Approach to Indoor Temperature Distribution Prediction of a Passive Solar Energy Efficient HouseGolden Makaka0University of Fort Hare, Private Bag Box X1314, Alice 5700, South AfricaWith the increase in energy consumption by buildings in keeping the indoor environment within the comfort levels and the ever increase of energy price there is need to design buildings that require minimal energy to keep the indoor environment within the comfort levels. There is need to predict the indoor temperature during the design stage. In this paper a statistical indoor temperature prediction model was developed. A passive solar house was constructed; thermal behaviour was simulated using ECOTECT and DOE computer software. The thermal behaviour of the house was monitored for a year. The indoor temperature was observed to be in the comfort level for 85% of the total time monitored. The simulation results were compared with the measured results and those from the prediction model. The statistical prediction model was found to agree (95%) with the measured results. Simulation results were observed to agree (96%) with the statistical prediction model. Modeled indoor temperature was most sensitive to the outdoor temperatures variations. The daily mean peak ones were found to be more pronounced in summer (5%) than in winter (4%). The developed model can be used to predict the instantaneous indoor temperature for a specific house design.http://dx.doi.org/10.1155/2015/128496
collection DOAJ
language English
format Article
sources DOAJ
author Golden Makaka
spellingShingle Golden Makaka
A Pedestrian Approach to Indoor Temperature Distribution Prediction of a Passive Solar Energy Efficient House
Journal of Renewable Energy
author_facet Golden Makaka
author_sort Golden Makaka
title A Pedestrian Approach to Indoor Temperature Distribution Prediction of a Passive Solar Energy Efficient House
title_short A Pedestrian Approach to Indoor Temperature Distribution Prediction of a Passive Solar Energy Efficient House
title_full A Pedestrian Approach to Indoor Temperature Distribution Prediction of a Passive Solar Energy Efficient House
title_fullStr A Pedestrian Approach to Indoor Temperature Distribution Prediction of a Passive Solar Energy Efficient House
title_full_unstemmed A Pedestrian Approach to Indoor Temperature Distribution Prediction of a Passive Solar Energy Efficient House
title_sort pedestrian approach to indoor temperature distribution prediction of a passive solar energy efficient house
publisher Hindawi Limited
series Journal of Renewable Energy
issn 2314-4386
2314-4394
publishDate 2015-01-01
description With the increase in energy consumption by buildings in keeping the indoor environment within the comfort levels and the ever increase of energy price there is need to design buildings that require minimal energy to keep the indoor environment within the comfort levels. There is need to predict the indoor temperature during the design stage. In this paper a statistical indoor temperature prediction model was developed. A passive solar house was constructed; thermal behaviour was simulated using ECOTECT and DOE computer software. The thermal behaviour of the house was monitored for a year. The indoor temperature was observed to be in the comfort level for 85% of the total time monitored. The simulation results were compared with the measured results and those from the prediction model. The statistical prediction model was found to agree (95%) with the measured results. Simulation results were observed to agree (96%) with the statistical prediction model. Modeled indoor temperature was most sensitive to the outdoor temperatures variations. The daily mean peak ones were found to be more pronounced in summer (5%) than in winter (4%). The developed model can be used to predict the instantaneous indoor temperature for a specific house design.
url http://dx.doi.org/10.1155/2015/128496
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