Prediction of indoor temperature and relative humidity using neural network models: model comparison

Lü, Tao; Viljanen, Martti

doi:10.1007/s00521-008-0185-3

Cited by 128 publications

(39 citation statements)

References 20 publications

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“…Soleimani-Mohseni et al [13] showed that the operative temperature could be well estimated by the ANN approach using the indoor air temperature, electrical power, outdoor temperature, time of day, wall temperature, and ventilation flow rate. Lu and Viljanen [14] used the ANN approach to predict air temperature and relative humidity in a test room using indoor and outdoor temperature and humidity. Recently, Zabada and Shahrour [15] used the ANN approach for the analysis of the heating expenses in social housing.…”

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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“…The parameters are obtained by experimental data and numerical calculations [10][11][12][13][14]. Reference [11] suggested a method to estimate the thermal parameters of building envelopes, especially the thermal capacitance of insulation materials of a wall by using a heat flow meter in laboratory.…”

Section: State-of-the-art Literaturementioning

confidence: 99%

“…Reference [13] was established several parametric models (ARX, ARMAX, BJ, and OE models) to identify the thermal behavior of an office and provided reasonably good predictions of indoor temperature and relative humidity. Moreover, [14] applied an ARX model and a neural network ARX model to the prediction of indoor temperature and relative humidity of an unoccupied residential building. These black-box modeling approaches do not require the physical properties and relying physical laws of the system to predict the system behavior, whereas the obtained parameters are not corresponding to any physical values of the system and only shows the mathematical relationship between inputs and outputs of the system.…”

Section: State-of-the-art Literaturementioning

confidence: 99%

Modeling of a Building System and its Parameter Identification

Park¹,

Martaj²,

Ruellan³

et al. 2013

Journal of Electrical Engineering and Technology

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-This study proposes a low order dynamic model of a building system in order to predict thermal behavior within a building and its energy consumption. The building system includes a thermally well-insulated room and an electric heater. It is modeled by a second order lumped RC thermal network based on the thermal-electrical analogy. In order to identify unknown parameters of the model, an experimental procedure is firstly detailed. Then, the different linear parametric models (ARMA, ARX, ARMAX, BJ, and OE models) are recalled. The parameters of the parametric models are obtained by the least square approach. The obtained parameters are interpreted to the parameters of the physically based model in accordance with their relationship. Afterwards, the obtained models are implemented in Matlab/Simulink® and are evaluated by the mean of the sum of absolute error (MAE) and the mean of the sum of square error (MSE) with the variable of indoor temperature of the room. Quantities of electrical energy and converted thermal energy are also compared. This study will permit a further study on Model Predictive Control adapting to the proposed model in order to reduce energy consumption of the building.

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“…Investigations were carried out by SoleimaniMohseni et al [13] to estimate the operative temperature in building using indoor air temperature, electrical power, outdoor temperature, time, wall's temperature and ventilation flow rate. Lu and Viljanen [14] used ANN to predict room air temperature and relative humidity. Recently, Zabada and Shahrour [15] used the ANN approach for the analysis of the heating expenses in social housing.…”

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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Prediction of indoor temperature and relative humidity using neural network models: model comparison

Cited by 128 publications

References 20 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

Modeling of a Building System and its Parameter Identification

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

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