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Estimation of Unmeasured Room Temperature, Relative Humidity, and CO 2 Concentrations for a Smart Building Using Machine Learning and Exploratory Data Analysis

Author

Listed:
  • Abraham Kaligambe

    (Power System Laboratory, Graduate School of Engineering and Science, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto City, Tokyo 135-8548, Japan)

  • Goro Fujita

    (Power System Laboratory, Graduate School of Engineering and Science, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto City, Tokyo 135-8548, Japan)

  • Tagami Keisuke

    (Technical Research Laboratory, DAI-DAN Co., Ltd., Saitama 354-0044, Japan)

Abstract

Smart buildings that utilize innovative technologies such as artificial intelligence (AI), the internet of things (IoT), and cloud computing to improve comfort and reduce energy waste are gaining popularity. Smart buildings comprise a range of sensors to measure real-time indoor environment variables essential for the heating, ventilation, and air conditioning (HVAC) system control strategies. For accuracy and smooth operation, current HVAC system control strategies require multiple sensors to capture the indoor environment variables. However, using too many sensors creates an extensive network that is costly and complex to maintain. Our proposed research solves the mentioned problem by implementing a machine-learning algorithm to estimate unmeasured variables utilizing a limited number of sensors. Using a six-month data set collected from a three-story smart building in Japan, several extreme gradient boosting (XGBoost) models were designed and trained to estimate unmeasured room temperature, relative humidity, and CO 2 concentrations. Our models accurately estimated temperature, humidity, and CO 2 concentration under various case studies with an average root mean squared error (RMSE) of 0.3 degrees, 2.6%, and 26.25 ppm, respectively. Obtained results show an accurate estimation of indoor environment measurements that is applicable for optimal HVAC system control in smart buildings with a reduced number of required sensors.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4213-:d:833728
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    References listed on IDEAS

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    1. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    2. 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.
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    Cited by:

    1. Sameh Mahjoub & Sami Labdai & Larbi Chrifi-Alaoui & Bruno Marhic & Laurent Delahoche, 2023. "Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network," Energies, MDPI, vol. 16(4), pages 1-18, February.
    2. Abdullah Al Abrar Chowdhury & Azizul Hakim Rafi & Adita Sultana & Abdulla All Noman, 2024. "Enhancing Green Economy with Artificial Intelligence: Role of Energy Use and FDI in the United States," Papers 2501.14747, arXiv.org.
    3. Ping Chen & Jiawei Gao & Zheng Ji & Han Liang & Yu Peng, 2022. "Do Artificial Intelligence Applications Affect Carbon Emission Performance?—Evidence from Panel Data Analysis of Chinese Cities," Energies, MDPI, vol. 15(15), pages 1-16, August.

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