The reality of English living rooms – A comparison of internal temperatures against common model assumptions

Huebner, Gesche M.; McMichael, Megan; Shipworth, David; Shipworth, Michelle; Durand-Daubin, Mathieu; Summerfield, Alex

doi:10.1016/j.enbuild.2013.07.025

Cited by 52 publications

(63 citation statements)

References 10 publications

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“…This forecasting constitutes a complex task, because it is governed by complex physical and behavioral phenomena. It is affected by a multitude of parameters, which could be classified into three groups: outdoor conditions, building characteristics, and occupants' behavior [3][4][5]. In addition, investigations showed that the indoor temperature does not have uniform distribution [6].…”

Section: Introductionmentioning

confidence: 99%

Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting

2018

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Abstract:The smart building concept aims to use smart technology to reduce energy consumption, as well as to improve comfort conditions and users' satisfaction. It is based on the use of smart sensors and software to follow both outdoor and indoor conditions for the control of comfort, and security devices for the optimization of energy consumption. This paper presents a data-based model for indoor temperature forecasting, which could be used for the optimization of energy device use. The model is based on an artificial neural network (ANN), which is validated on data recorded in an old building. The novelty of this work consists of the methodology proposed for the development of a simplified model for indoor temperature forecasting. This methodology is based on the selection of pertinent input parameters after a relevance analysis of a large set of input parameters, including solar radiation outdoor temperature history, outdoor humidity, indoor facade temperature, and humidity. It shows that an ANN-based model using outdoor and facade temperature sensors provides good forecasting of indoor temperatures. This model can be easily used in the optimal regulation of buildings' energy devices.

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Section: Introductionmentioning

confidence: 99%

Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting

2018

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“…In [9], the comparison of room temperature with the values that were used for the heating design of these rooms is performed. This comparison showed that the real value of room temperature in average was less than taken for heating design.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Analysis of the temperature distribution in a space heated by a dynamic (fan) storage heater

Lysak¹

2017

EEJET

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“…The indoor temperature forecasting constitutes a complex task, because it is governed by complex physical and behavioral phenomena. It is affected by a multitude of parameters, which can be classified into three groups: outdoor conditions, building characteristics and occupants' behavior [3][4][5]. In addition, investigations showed that the indoor temperature doesn't have uniform distribution [6].…”

Section: Introductionmentioning

confidence: 99%

Smart Building: Use of the ANN Approach for Indoor Temperature Forecasting

Attoue¹,

Shahrour²,

Younès³

2018

Preprint

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Smart buildings concept aims at the use of the smart technology to reduce energy consumption as well as improvement of the comfort conditions and users' satisfaction. It is based on the use of smart sensors to follow both outdoor and indoor conditions as well as software for the control of comfort and security devices. The optimal control of the energy devices requires software for indoor temperature forecasting. This paper presents an ANN -based model for the indoor temperature forecasting. The model is developed using data recoded in an old building of the engineering school Polytech'Lille. Data covered both indoor and outdoor conditions. Analysis of the relevance of the input parameters allowed to develop a simplified forecasting model of the indoor temperature that uses only the outdoor temperature as well as the history of the façade temperature as input parameters. The paper presents successively, data collection, the ANN concept used in the temperature forecasting, and finally the ANN model developed for the façade and indoor forecasting. It shows that an ANN-based model using outdoor and façade temperature sensors provides a good forecasting of the indoor temperature. This model could be used for the optimal control of buildings energy devices.

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The reality of English living rooms – A comparison of internal temperatures against common model assumptions

Cited by 52 publications

References 10 publications

Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting

Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting

Analysis of the temperature distribution in a space heated by a dynamic (fan) storage heater

Smart Building: Use of the ANN Approach for Indoor Temperature Forecasting

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