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

Author

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  • Nivine Attoue

    (Laboratory of Civil Engineering and Geo-Environment, Lille University, 59650 Villeneuve d’Ascq, France)

  • Isam Shahrour

    (Laboratory of Civil Engineering and Geo-Environment, Lille University, 59650 Villeneuve d’Ascq, France
    School of Civil Engineering, Tongji University, Shanghai 200092, China)

  • Rafic Younes

    (Modeling Center, Lebanese University, Hadath 99000, Lebanon)

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.

Suggested Citation

  • Nivine Attoue & Isam Shahrour & Rafic Younes, 2018. "Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting," Energies, MDPI, vol. 11(2), pages 1-12, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:395-:d:130903
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    References listed on IDEAS

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    2. Shaker Zabada & Isam Shahrour, 2017. "Analysis of Heating Expenses in a Large Social Housing Stock Using Artificial Neural Networks," Energies, MDPI, vol. 10(12), pages 1-8, December.
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    Cited by:

    1. Abraham Kaligambe & Goro Fujita & Tagami Keisuke, 2022. "Estimation of Unmeasured Room Temperature, Relative Humidity, and CO 2 Concentrations for a Smart Building Using Machine Learning and Exploratory Data Analysis," Energies, MDPI, vol. 15(12), pages 1-12, June.
    2. Yue, Naihua & Caini, Mauro & Li, Lingling & Zhao, Yang & Li, Yu, 2023. "A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions," Applied Energy, Elsevier, vol. 332(C).
    3. Martín Pensado-Mariño & Lara Febrero-Garrido & Pablo Eguía-Oller & Enrique Granada-Álvarez, 2021. "Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    4. Rasa Džiugaitė-Tumėnienė & Rūta Mikučionienė & Giedrė Streckienė & Juozas Bielskus, 2021. "Development and Analysis of a Dynamic Energy Model of an Office Using a Building Management System (BMS) and Actual Measurement Data," Energies, MDPI, vol. 14(19), pages 1-24, October.
    5. Mohd. Ahmed & Saeed AlQadhi & Javed Mallick & Nabil Ben Kahla & Hoang Anh Le & Chander Kumar Singh & Hoang Thi Hang, 2022. "Artificial Neural Networks for Sustainable Development of the Construction Industry," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    6. Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
    7. Muhammad Ali & Krishneel Prakash & Carlos Macana & Ali Kashif Bashir & Alireza Jolfaei & Awais Bokhari & Jiří Jaromír Klemeš & Hemanshu Pota, 2022. "Modeling Residential Electricity Consumption from Public Demographic Data for Sustainable Cities," Energies, MDPI, vol. 15(6), pages 1-16, March.
    8. Petri Hietaharju & Mika Ruusunen & Kauko Leiviskä, 2018. "A Dynamic Model for Indoor Temperature Prediction in Buildings," Energies, MDPI, vol. 11(6), pages 1-20, June.
    9. Xike Zhang & Qiuwen Zhang & Gui Zhang & Zhiping Nie & Zifan Gui & Huafei Que, 2018. "A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition," IJERPH, MDPI, vol. 15(5), pages 1-23, May.
    10. Dana-Mihaela Petroșanu & George Căruțașu & Nicoleta Luminița Căruțașu & Alexandru Pîrjan, 2019. "A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal B," Energies, MDPI, vol. 12(24), pages 1-64, December.
    11. David Bienvenido-Huertas & Carlos Rubio-Bellido & Juan Luis Pérez-Ordóñez & Fernando Martínez-Abella, 2019. "Estimating Adaptive Setpoint Temperatures Using Weather Stations," Energies, MDPI, vol. 12(7), pages 1-47, March.
    12. Feng Xu & Kei Sakurai & Yuki Sato & Yuka Sakai & Shunsuke Sabu & Hiroaki Kanayama & Daisuke Satou & Yasuki Kansha, 2023. "Soft-Sensor Modeling of Temperature Variation in a Room under Cooling Conditions," Energies, MDPI, vol. 16(6), pages 1-13, March.
    13. Juan Botero-Valencia & Luis Castano-Londono & David Marquez-Viloria, 2022. "Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments," Data, MDPI, vol. 7(6), pages 1-15, June.
    14. Kefan Huang & Kevin P. Hallinan & Robert Lou & Abdulrahman Alanezi & Salahaldin Alshatshati & Qiancheng Sun, 2020. "Self-Learning Algorithm to Predict Indoor Temperature and Cooling Demand from Smart WiFi Thermostat in a Residential Building," Sustainability, MDPI, vol. 12(17), pages 1-14, August.
    15. Byung Kyu Park & Charn-Jung Kim, 2023. "Short-Term Prediction for Indoor Temperature Control Using Artificial Neural Network," Energies, MDPI, vol. 16(23), pages 1-17, November.
    16. Song, Jiancai & Bian, Tianxiang & Xue, Guixiang & Wang, Hanyu & Shen, Xingliang & Wu, Xiangdong, 2023. "Short-term forecasting model for residential indoor temperature in DHS based on sequence generative adversarial network," Applied Energy, Elsevier, vol. 348(C).
    17. López-Pérez, Luis Adrián & Flores-Prieto, José Jassón, 2023. "Adaptive thermal comfort approach to save energy in tropical climate educational building by artificial intelligence," Energy, Elsevier, vol. 263(PA).

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