IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i21p4187-d282965.html
   My bibliography  Save this article

Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection

Author

Listed:
  • Jihoon Jang

    (Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Joosang Lee

    (Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Eunjo Son

    (Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Kyungyong Park

    (Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Gahee Kim

    (Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Jee Hang Lee

    (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea)

  • Seung-Bok Leigh

    (Department of Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

Abstract

Humans spend approximately 90% of the daytime in buildings, and greenhouse gases (GHGs) emitted by buildings account for approximately 20% of total GHG emissions. As the energy consumed during building operation from a building life-cycle perspective amounts to approximately 70–90% of the total energy, it is essential to accurately predict the energy consumption of buildings for their efficient operation. This study aims to optimize a model for predicting the thermal energy consumption of buildings by (i) first extracting major variables through feature selection and deriving significant variables in addition to the collected data and (ii) predicting the thermal energy consumption using a machine learning model. Feature selection using random forest was performed, and 11 out of 17 available data were selected. The accuracy of the prediction model was significantly improved when the hour of day variable was added. The prediction model was constructed using an artificial neural network (ANN), and the improvement in the prediction accuracy was analyzed by comparing different cases of variable combinations. The ANN prediction accuracy was improved by 15% using the feature selection process compared to when all data were used as input data, and 25% coefficient of variation of the root mean square error (CVRMSE) accuracy was achieved.

Suggested Citation

  • Jihoon Jang & Joosang Lee & Eunjo Son & Kyungyong Park & Gahee Kim & Jee Hang Lee & Seung-Bok Leigh, 2019. "Development of an Improved Model to Predict Building Thermal Energy Consumption by Utilizing Feature Selection," Energies, MDPI, vol. 12(21), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4187-:d:282965
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/21/4187/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/21/4187/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    3. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    4. Kafle, Sagar & Parajuli, Ranjan & Bhattarai, Sujala & Euh, Seung Hee & Kim, Dae Hyun, 2017. "A review on energy systems and GHG emissions reduction plan and policy of the Republic of Korea: Past, present, and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1123-1130.
    5. Kim, Min-Hwi & Kim, Deukwon & Heo, Jaehyeok & Lee, Dong-Won, 2020. "Energy performance investigation of net plus energy town: Energy balance of the Jincheon Eco-Friendly energy town," Renewable Energy, Elsevier, vol. 147(P1), pages 1784-1800.
    6. Sooyoun Cho & Jeehang Lee & Jumi Baek & Gi-Seok Kim & Seung-Bok Leigh, 2019. "Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach," Energies, MDPI, vol. 12(21), pages 1-23, October.
    7. Almonacid, F. & Rus, C. & Pérez-Higueras, P. & Hontoria, L., 2011. "Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks," Energy, Elsevier, vol. 36(1), pages 375-384.
    8. Powell, Kody M. & Sriprasad, Akshay & Cole, Wesley J. & Edgar, Thomas F., 2014. "Heating, cooling, and electrical load forecasting for a large-scale district energy system," Energy, Elsevier, vol. 74(C), pages 877-885.
    9. Kialashaki, Arash & Reisel, John R., 2014. "Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States," Energy, Elsevier, vol. 76(C), pages 749-760.
    10. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    11. Kim, Min-Hwi & Kim, Deukwon & Heo, Jaehyeok & Lee, Dong-Won, 2019. "Techno-economic analysis of hybrid renewable energy system with solar district heating for net zero energy community," Energy, Elsevier, vol. 187(C).
    12. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    13. Jiang, Ping & Liu, Feng & Song, Yiliao, 2017. "A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting," Energy, Elsevier, vol. 119(C), pages 694-709.
    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. Jun-Mao Liao & Ming-Jui Chang & Luh-Maan Chang, 2020. "Prediction of Air-Conditioning Energy Consumption in R&D Building Using Multiple Machine Learning Techniques," Energies, MDPI, vol. 13(7), pages 1-22, April.
    2. Zihao Li & Daniel Friedrich & Gareth P. Harrison, 2020. "Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model," Energies, MDPI, vol. 13(4), pages 1-20, February.
    3. Lee-Yong Sung & Jonghoon Ahn, 2020. "Comparative Analyses of Energy Efficiency between on-Demand and Predictive Controls for Buildings’ Indoor Thermal Environment," Energies, MDPI, vol. 13(5), pages 1-15, March.
    4. 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.
    5. Jihoon Jang & Sukumar Natarajan & Joosang Lee & Seung-Bok Leigh, 2022. "Comparative Analysis of Overheating Risk for Typical Dwellings and Passivhaus in the UK," Energies, MDPI, vol. 15(10), pages 1-22, May.

    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. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    2. Kljajić, Miroslav & Gvozdenac, Dušan & Vukmirović, Srdjan, 2012. "Use of Neural Networks for modeling and predicting boiler's operating performance," Energy, Elsevier, vol. 45(1), pages 304-311.
    3. Seok-Jun Bu & Sung-Bae Cho, 2020. "Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption," Energies, MDPI, vol. 13(18), pages 1-16, September.
    4. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    5. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    6. Chaturvedi, Shobhit & Rajasekar, Elangovan & Natarajan, Sukumar & McCullen, Nick, 2022. "A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India," Energy Policy, Elsevier, vol. 168(C).
    7. Min-Hwi Kim & Dong-Won Lee & Deuk-Won Kim & Young-Sub An & Jae-Ho Yun, 2021. "Energy Performance Investigation of Bi-Directional Convergence Energy Prosumers for an Energy Sharing Community," Energies, MDPI, vol. 14(17), pages 1-17, September.
    8. Vincenzo Bianco & Annalisa Marchitto & Federico Scarpa & Luca A. Tagliafico, 2020. "Forecasting Energy Consumption in the EU Residential Sector," IJERPH, MDPI, vol. 17(7), pages 1-15, March.
    9. Movagharnejad, Kamyar & Mehdizadeh, Bahman & Banihashemi, Morteza & Kordkheili, Masoud Sheikhi, 2011. "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network," Energy, Elsevier, vol. 36(7), pages 3979-3984.
    10. Chen, Guangwu & Zhu, Yuhan & Wiedmann, Thomas & Yao, Lina & Xu, Lixiao & Wang, Yafei, 2019. "Urban-rural disparities of household energy requirements and influence factors in China: Classification tree models," Applied Energy, Elsevier, vol. 250(C), pages 1321-1335.
    11. Almonacid, F. & Rus, C. & Pérez-Higueras, P. & Hontoria, L., 2011. "Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks," Energy, Elsevier, vol. 36(1), pages 375-384.
    12. Jasiński, Tomasz, 2019. "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, vol. 179(C), pages 831-842.
    13. Min-Hwi Kim & Deuk-Won Kim & Dong-Won Lee, 2021. "Feasibility of Low Carbon Renewable Energy City Integrated with Hybrid Renewable Energy Systems," Energies, MDPI, vol. 14(21), pages 1-24, November.
    14. Wu, Xuedong & Mao, Jianxu & Du, Zhaoping & Chang, Yanchao, 2013. "Online training algorithms based single multiplicative neuron model for energy consumption forecasting," Energy, Elsevier, vol. 59(C), pages 126-132.
    15. Ahmad, Tanveer & Huanxin, Chen & Zhang, Dongdong & Zhang, Hongcai, 2020. "Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions," Energy, Elsevier, vol. 198(C).
    16. Shepero, Mahmoud & van der Meer, Dennis & Munkhammar, Joakim & Widén, Joakim, 2018. "Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data," Applied Energy, Elsevier, vol. 218(C), pages 159-172.
    17. SeyedAli Ghahari & Cesar Queiroz & Samuel Labi & Sue McNeil, 2021. "Cluster Forecasting of Corruption Using Nonlinear Autoregressive Models with Exogenous Variables (NARX)—An Artificial Neural Network Analysis," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
    18. Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.
    19. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
    20. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).

    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:12:y:2019:i:21:p:4187-:d:282965. 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.