IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i2p999-d1037576.html
   My bibliography  Save this article

Load Prediction Algorithm Applied with Indoor Environment Sensing in University Buildings

Author

Listed:
  • Yunho Kim

    (School of Architectural, Civil, Environmental, and Energy Engineering, Kyungpook National University, 80 Daehakro, Buk-gu, Daegu 41566, Republic of Korea)

  • Yunha Park

    (School of Architectural, Civil, Environmental, and Energy Engineering, Kyungpook National University, 80 Daehakro, Buk-gu, Daegu 41566, Republic of Korea)

  • Hyuncheol Seo

    (School of Architectural, Civil, Environmental, and Energy Engineering, Kyungpook National University, 80 Daehakro, Buk-gu, Daegu 41566, Republic of Korea)

  • Jungha Hwang

    (School of Architectural, Civil, Environmental, and Energy Engineering, Kyungpook National University, 80 Daehakro, Buk-gu, Daegu 41566, Republic of Korea)

Abstract

Recently, building automation system (BAS) and building energy management system (BEMS) technologies have been applied to efficiently reduce the energy consumption of buildings. In addition, studies on utilizing large quantities of building data have been actively conducted using artificial intelligence and machine learning. However, the high cost and installation difficulties limit the use of measuring devices to sense the indoor environment of all buildings. Therefore, this study developed a comprehensive indoor environment sensor module with relatively inexpensive sensors to measure the indoor environment of a university building. In addition, an algorithm for predicting the load in real time through machine learning based on indoor environment measurement is proposed. When the reliability of the algorithm for predicting the number of occupants and load according to the indoor CO 2 concentration was quantitatively assessed, the mean squared error (MSE), root mean square deviation (RMSD), and mean absolute error (MAE) were calculated to be 23.1, 4.8, and 2.5, respectively, indicating the high accuracy of the algorithm. Since the sensor used in this study is economical and can be easily applied to existing buildings, it is expected to be favorable for the dissemination of load prediction technology.

Suggested Citation

  • Yunho Kim & Yunha Park & Hyuncheol Seo & Jungha Hwang, 2023. "Load Prediction Algorithm Applied with Indoor Environment Sensing in University Buildings," Energies, MDPI, vol. 16(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:999-:d:1037576
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/2/999/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/2/999/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Krzysztof Grygierek & Joanna Ferdyn-Grygierek, 2022. "Design of Ventilation Systems in a Single-Family House in Terms of Heating Demand and Indoor Environment Quality," Energies, MDPI, vol. 15(22), pages 1-18, November.
    2. Michele Zinzi & Francesca Pagliaro & Stefano Agnoli & Fabio Bisegna & Domenico Iatauro, 2021. "On the Built-Environment Quality in Nearly Zero-Energy Renovated Schools: Assessment and Impact of Passive Strategies," Energies, MDPI, vol. 14(10), pages 1-18, May.
    3. Eunji Kim & Yoonhee Ha, 2021. "Vitalization Strategies for the Building Energy Management System (BEMS) Industry Ecosystem Based on AHP Analysis," Energies, MDPI, vol. 14(9), pages 1-16, April.
    4. Karim Mohamed Ragab & Mehmet Fatih Orhan & Kenan Saka & Yousef Zurigat, 2022. "A Study and Assessment of the Status of Energy Efficiency and Conservation at School Buildings," Sustainability, MDPI, vol. 14(17), pages 1-31, August.
    5. Mohammad Aldekheel & Abdulmalik Altuwayjiri & Ramin Tohidi & Vahid Jalali Farahani & Constantinos Sioutas, 2022. "The Role of Portable Air Purifiers and Effective Ventilation in Improving Indoor Air Quality in University Classrooms," IJERPH, MDPI, vol. 19(21), pages 1-17, November.
    6. Wenxiao Chu & Maria Vicidomini & Francesco Calise & Neven Duić & Poul Alborg Østergaard & Qiuwang Wang & Maria da Graça Carvalho, 2022. "Recent Advances in Technologies, Methods, and Economic Analysis for Sustainable Development of Energy, Water, and Environment Systems," Energies, MDPI, vol. 15(19), pages 1-24, September.
    7. Alessandro Franco & Carlo Bartoli & Paolo Conti & Lorenzo Miserocchi & Daniele Testi, 2021. "Multi-Objective Optimization of HVAC Operation for Balancing Energy Use and Occupant Comfort in Educational Buildings," Energies, MDPI, vol. 14(10), pages 1-19, May.
    8. Jin Sol Hwang & Ismi Rosyiana Fitri & Jung-Su Kim & Hwachang Song, 2020. "Optimal ESS Scheduling for Peak Shaving of Building Energy Using Accuracy-Enhanced Load Forecast," Energies, MDPI, vol. 13(21), pages 1-18, October.
    9. Nasir Ayub & Muhammad Irfan & Muhammad Awais & Usman Ali & Tariq Ali & Mohammed Hamdi & Abdullah Alghamdi & Fazal Muhammad, 2020. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler," Energies, MDPI, vol. 13(19), pages 1-21, October.
    10. Hossein Moayedi & Amir Mosavi, 2021. "Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings," Energies, MDPI, vol. 14(5), pages 1-25, March.
    11. Anam-Nawaz Khan & Naeem Iqbal & Atif Rizwan & Rashid Ahmad & Do-Hyeun Kim, 2021. "An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings," Energies, MDPI, vol. 14(11), pages 1-25, May.
    12. Branko Simanic & Birgitta Nordquist & Hans Bagge & Dennis Johansson, 2020. "Influence of User-Related Parameters on Calculated Energy Use in Low-Energy School Buildings," Energies, MDPI, vol. 13(11), pages 1-14, June.
    13. Fathia Chekired & Oussama Taabli & Zakaria Mehdi Khellili & Amar Tilmatine & Aníbal T. de Almeida & Laurent Canale, 2022. "Near-Zero-Energy Building Management Based on Arduino Microcontroller—On-Site Lighting Management Application," Energies, MDPI, vol. 15(23), pages 1-20, November.
    14. Francesco Mancini & Fabio Nardecchia & Daniele Groppi & Francesco Ruperto & Carlo Romeo, 2020. "Indoor Environmental Quality Analysis for Optimizing Energy Consumptions Varying Air Ventilation Rates," Sustainability, MDPI, vol. 12(2), pages 1-18, January.
    Full references (including those not matched with items on IDEAS)

    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. Neilson Luniere Vilaça & Marly Guimarães Fernandes Costa & Cicero Ferreira Fernandes Costa Filho, 2023. "A Hybrid Deep Neural Network Architecture for Day-Ahead Electricity Forecasting: Post-COVID Paradigm," Energies, MDPI, vol. 16(8), pages 1-14, April.
    2. Seif Khiati & Rafik Belarbi & Ammar Yahia, 2023. "Sustainable Buildings: A Choice, or a Must for Our Future?," Energies, MDPI, vol. 16(6), pages 1-5, March.
    3. Anna Borkovcová & Miloslava Černá & Marcela Sokolová, 2022. "Blockchain in the Energy Sector—Systematic Review," Sustainability, MDPI, vol. 14(22), pages 1-12, November.
    4. Reza Alayi & Mahdi Mohkam & Seyed Reza Seyednouri & Mohammad Hossein Ahmadi & Mohsen Sharifpur, 2021. "Energy/Economic Analysis and Optimization of On-Grid Photovoltaic System Using CPSO Algorithm," Sustainability, MDPI, vol. 13(22), pages 1-16, November.
    5. Aswin Balasubramanian & Floran Martin & Md Masum Billah & Osaruyi Osemwinyen & Anouar Belahcen, 2021. "Application of Surrogate Optimization Routine with Clustering Technique for Optimal Design of an Induction Motor," Energies, MDPI, vol. 14(16), pages 1-19, August.
    6. Habib Kraiem & Aymen Flah & Naoui Mohamed & Mohamed H. B. Messaoud & Essam A. Al-Ammar & Ahmed Althobaiti & Abdullah Alhumaidi Alotaibi & Michał Jasiński & Vishnu Suresh & Zbigniew Leonowicz & Elżbiet, 2022. "Decreasing the Battery Recharge Time if Using a Fuzzy Based Power Management Loop for an Isolated Micro-Grid Farm," Sustainability, MDPI, vol. 14(5), pages 1-21, March.
    7. Jonghoon Ahn, 2020. "Improvement of the Performance Balance between Thermal Comfort and Energy Use for a Building Space in the Mid-Spring Season," Sustainability, MDPI, vol. 12(22), pages 1-14, November.
    8. 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.
    9. Aminhossein Jahanbin & Giovanni Semprini, 2020. "Numerical Study on Indoor Environmental Quality in a Room Equipped with a Combined HRV and Radiator System," Sustainability, MDPI, vol. 12(24), pages 1-22, December.
    10. Seyedeh Farzaneh Mousavi Motlagh & Ali Sohani & Mohammad Djavad Saghafi & Hoseyn Sayyaadi & Benedetto Nastasi, 2021. "The Road to Developing Economically Feasible Plans for Green, Comfortable and Energy Efficient Buildings," Energies, MDPI, vol. 14(3), pages 1-30, January.
    11. Michał Jasiński & Arsalan Najafi & Tomasz Sikorski & Paweł Kostyła & Jacek Rezmer, 2022. "Operation of an Energy Storage System Integrated with a Photovoltaic System and an Industrial Customer under Different Real and Pseudo-Real Profiles," Energies, MDPI, vol. 15(21), pages 1-27, November.
    12. Yang, Yiran & Li, Gang & Luo, Tao & Al-Bahrani, Mohammed & Al-Ammar, Essam A. & Sillanpaa, Mika & Ali, Shafaqat & Leng, Xiujuan, 2023. "The innovative optimization techniques for forecasting the energy consumption of buildings using the shuffled frog leaping algorithm and different neural networks," Energy, Elsevier, vol. 268(C).
    13. Loke Kok Foong & Binh Nguyen Le, 2022. "Teaching–Learning–Based Optimization (TLBO) in Hybridized with Fuzzy Inference System Estimating Heating Loads," Energies, MDPI, vol. 15(21), pages 1-20, November.
    14. Mahmoud Khaled & Samer Ali & Hassan Jaber & Jalal Faraj & Rabih Murr & Thierry Lemenand, 2022. "Heating/Cooling Fresh Air Using Hot/Cold Exhaust Air of Heating, Ventilating, and Air Conditioning Systems," Energies, MDPI, vol. 15(5), pages 1-11, March.
    15. Rajavelu Dharani & Madasamy Balasubramonian & Thanikanti Sudhakar Babu & Benedetto Nastasi, 2021. "Load Shifting and Peak Clipping for Reducing Energy Consumption in an Indian University Campus," Energies, MDPI, vol. 14(3), pages 1-16, January.
    16. Alessandro Franco & Lorenzo Miserocchi & Daniele Testi, 2021. "HVAC Energy Saving Strategies for Public Buildings Based on Heat Pumps and Demand Controlled Ventilation," Energies, MDPI, vol. 14(17), pages 1-20, September.
    17. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    18. Sajawal ur Rehman Khan & Israa Adil Hayder & Muhammad Asif Habib & Mudassar Ahmad & Syed Muhammad Mohsin & Farrukh Aslam Khan & Kainat Mustafa, 2022. "Enhanced Machine-Learning Techniques for Medium-Term and Short-Term Electric-Load Forecasting in Smart Grids," Energies, MDPI, vol. 16(1), pages 1-16, December.
    19. Jonghoon Ahn, 2020. "Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    20. Alessandro Franco & Carlo Bartoli & Paolo Conti & Daniele Testi, 2021. "Optimal Operation of Low-Capacity Heat Pump Systems for Residential Buildings through Thermal Energy Storage," Sustainability, MDPI, vol. 13(13), pages 1-17, June.

    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:16:y:2023:i:2:p:999-:d:1037576. 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.