IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i12p4213-d833728.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/12/4213/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/12/4213/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
    2. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    3. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2019. "Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation," Energies, MDPI, vol. 12(18), pages 1-19, September.
    4. Rafael Sánchez-Durán & Joaquín Luque & Julio Barbancho, 2019. "Long-Term Demand Forecasting in a Scenario of Energy Transition," Energies, MDPI, vol. 12(16), pages 1-23, August.
    5. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    6. 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).
    7. Arash Moradzadeh & Sahar Zakeri & Maryam Shoaran & Behnam Mohammadi-Ivatloo & Fazel Mohammadi, 2020. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    8. Jiseong Noh & Hyun-Ji Park & Jong Soo Kim & Seung-June Hwang, 2020. "Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management," Mathematics, MDPI, vol. 8(4), pages 1-14, April.
    9. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    10. Kostadin Yotov & Emil Hadzhikolev & Stanka Hadzhikoleva & Stoyan Cheresharov, 2022. "Neuro-Cybernetic System for Forecasting Electricity Consumption in the Bulgarian National Power System," Sustainability, MDPI, vol. 14(17), pages 1-18, September.
    11. 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.
    12. Stanislaw Osowski & Robert Szmurlo & Krzysztof Siwek & Tomasz Ciechulski, 2022. "Neural Approaches to Short-Time Load Forecasting in Power Systems—A Comparative Study," Energies, MDPI, vol. 15(9), pages 1-21, April.
    13. Ng, Rong Wang & Begam, Kasim Mumtaj & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2021. "An improved self-organizing incremental neural network model for short-term time-series load prediction," Applied Energy, Elsevier, vol. 292(C).
    14. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    15. Thomas Steens & Jan-Simon Telle & Benedikt Hanke & Karsten von Maydell & Carsten Agert & Gian-Luca Di Modica & Bernd Engel & Matthias Grottke, 2021. "A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV," Energies, MDPI, vol. 14(12), pages 1-25, June.
    16. Andreea Valeria Vesa & Tudor Cioara & Ionut Anghel & Marcel Antal & Claudia Pop & Bogdan Iancu & Ioan Salomie & Vasile Teodor Dadarlat, 2020. "Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs," Sustainability, MDPI, vol. 12(4), pages 1-23, February.
    17. 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.
    18. Mehmet Türker Takcı & Tuba Gözel, 2022. "Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
    19. Gülsah Erdogan & Wiem Fekih Hassen, 2023. "Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations," Energies, MDPI, vol. 16(18), pages 1-29, September.
    20. Shahzad Aslam & Nasir Ayub & Umer Farooq & Muhammad Junaid Alvi & Fahad R. Albogamy & Gul Rukh & Syed Irtaza Haider & Ahmad Taher Azar & Rasool Bukhsh, 2021. "Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid," Sustainability, MDPI, vol. 13(22), pages 1-28, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4213-:d:833728. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.