Preprint Article Version 1 This version not peer reviewed

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

Version 1 : Received: 6 January 2018 / Approved: 8 January 2018 / Online: 8 January 2018 (08:58:09 CET)

A peer-reviewed article of this Preprint also exists.

Attoue, N.; Shahrour, I.; Younes, R. Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting. Energies 2018, 11, 395. Attoue, N.; Shahrour, I.; Younes, R. Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting. Energies 2018, 11, 395.

Journal reference: Energies 2018, 11, 395
DOI: 10.3390/en11020395

Abstract

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.

Subject Areas

smart building; artificial neural network (ANN); indoor; temperature; façade; outdoor; forecasting; relevance; sensors; recorded data

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