IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i23p13258-d691699.html
   My bibliography  Save this article

Development of an Electrical Energy Consumption Model for Malaysian Households, Based on Techno-Socioeconomic Determinant Factors

Author

Listed:
  • Boni Sena

    (Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
    Faculty of Engineering, University of Singaperbangsa Karawang, Karawang 41361, Indonesia)

  • Sheikh Ahmad Zaki

    (Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

  • Hom Bahadur Rijal

    (Faculty of Environmental Studies, Tokyo City University, Yokohama 224-8551, Japan)

  • Jorge Alfredo Ardila-Rey

    (Department of Electrical Engineering, Universidad Técnica Federico Santa María, Santiago de Chile 8940000, Chile)

  • Nelidya Md Yusoff

    (Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

  • Fitri Yakub

    (Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

  • Farah Liana

    (Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

  • Mohamad Zaki Hassan

    (Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia)

Abstract

Energy-saving strategies are required to address the increasing global CO 2 and electrical energy consumption problems. Therefore, the determinant factors of electrical energy consumption consist of socio-demographic changes, occupant behavior, house and appliance characteristics, or so-called techno-socioeconomic factors, which all need to be assessed. Statistics models, such as the artificial neural network (ANN), can investigate the relationship among those factors. However, the previous ANN model only used limited factors and was conducted in the developed countries of subtropical regions with different determinant factors than those in the developing countries of tropical regions. Furthermore, the previous studies did not investigate the various impacts of techno-socioeconomic factors concerning the performance of the ANN model in estimating monthly electrical energy consumption. The current study develops a model with a more-in depth architecture by examining the effect of additional factors such as socio-demographics, house characteristics, occupant behavior, and appliance characteristics that have not been investigated concerning the model performance. Thus, a questionnaire survey was conducted from November 2017 to January 2018 with 214 university students. The best combination factors in explaining the monthly electrical energy consumption were developed from occupant behavior, with 81% of the variance and a mean absolute percentage error (MAPE) of 20.6%, which can be classified as a reasonably accurate model. The current study’s findings could be used as additional information for occupants or for companies who want to install photovoltaic or wind energy systems.

Suggested Citation

  • Boni Sena & Sheikh Ahmad Zaki & Hom Bahadur Rijal & Jorge Alfredo Ardila-Rey & Nelidya Md Yusoff & Fitri Yakub & Farah Liana & Mohamad Zaki Hassan, 2021. "Development of an Electrical Energy Consumption Model for Malaysian Households, Based on Techno-Socioeconomic Determinant Factors," Sustainability, MDPI, vol. 13(23), pages 1-22, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13258-:d:691699
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/23/13258/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/23/13258/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Seunghui Lee & Sungwon Jung & Jaewook Lee, 2019. "Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea," Energies, MDPI, vol. 12(4), pages 1-18, February.
    2. Vivian W. Y. Tam & Laura Almeida & Khoa Le, 2018. "Energy-Related Occupant Behaviour and Its Implications in Energy Use: A Chronological Review," Sustainability, MDPI, vol. 10(8), pages 1-20, July.
    3. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    4. Sheikh Ahmad Zaki & Siti Wan Syahidah & Mohd Fairuz Shahidan & Mardiana Idayu Ahmad & Fitri Yakub & Mohamad Zaki Hassan & Mohd Yusof Md Daud, 2020. "Assessment of Outdoor Air Temperature with Different Shaded Area within an Urban University Campus in Hot-Humid Climate," Sustainability, MDPI, vol. 12(14), pages 1-24, July.
    5. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    6. Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
    7. Boni Sena & Sheikh Ahmad Zaki & Hom Bahadur Rijal & Jorge Alfredo Ardila-Rey & Nelidya Md Yusoff & Fitri Yakub & Mohammad Kholid Ridwan & Firdaus Muhammad-Sukki, 2021. "Determinant Factors of Electricity Consumption for a Malaysian Household Based on a Field Survey," Sustainability, MDPI, vol. 13(2), pages 1-31, January.
    8. Sol Kim & Sungwon Jung & Seung-Man Baek, 2019. "A Model for Predicting Energy Usage Pattern Types with Energy Consumption Information According to the Behaviors of Single-Person Households in South Korea," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    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. Surajet Khonjun & Rapeepan Pitakaso & Kanchana Sethanan & Natthapong Nanthasamroeng & Kiatisak Pranet & Chutchai Kaewta & Ponglert Sangkaphet, 2022. "Differential Evolution Algorithm for Optimizing the Energy Usage of Vertical Transportation in an Elevator (VTE), Taking into Consideration Rush Hour Management and COVID-19 Prevention," Sustainability, MDPI, vol. 14(5), pages 1-19, February.

    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. Chen, Chien-fei & Xu, Xiaojing & Adua, Lazarus & Briggs, Morgan & Nelson, Hannah, 2022. "Exploring the factors that influence energy use intensity across low-, middle-, and high-income households in the United States," Energy Policy, Elsevier, vol. 168(C).
    2. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    3. Beccali, M. & Bonomolo, M. & Ciulla, G. & Lo Brano, V., 2018. "Assessment of indoor illuminance and study on best photosensors' position for design and commissioning of Daylight Linked Control systems. A new method based on artificial neural networks," Energy, Elsevier, vol. 154(C), pages 466-476.
    4. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    5. 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).
    6. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
    7. Soutullo, S. & Giancola, E. & Heras, M.R., 2018. "Dynamic energy assessment to analyze different refurbishment strategies of existing dwellings placed in Madrid," Energy, Elsevier, vol. 152(C), pages 1011-1023.
    8. Jonas Bielskus & Violeta Motuzienė & Tatjana Vilutienė & Audrius Indriulionis, 2020. "Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model," Energies, MDPI, vol. 13(15), pages 1-20, August.
    9. Qi Dong & Kai Xing & Hongrui Zhang, 2017. "Artificial Neural Network for Assessment of Energy Consumption and Cost for Cross Laminated Timber Office Building in Severe Cold Regions," Sustainability, MDPI, vol. 10(1), pages 1-15, December.
    10. Ahmed Gassar, Abdo Abdullah & Yun, Geun Young & Kim, Sumin, 2019. "Data-driven approach to prediction of residential energy consumption at urban scales in London," Energy, Elsevier, vol. 187(C).
    11. Morgane Innocent & Agnès François-Lecompte & Nolwenn Roudaut, 2020. "Comparison of human versus technological support to reduce domestic electricity consumption in France," Post-Print hal-02450849, HAL.
    12. Małgorzata Sztorc, 2022. "The Implementation of the European Green Deal Strategy as a Challenge for Energy Management in the Face of the COVID-19 Pandemic," Energies, MDPI, vol. 15(7), pages 1-21, April.
    13. Roberta Pernetti & Riccardo Pinotti & Roberto Lollini, 2021. "Repository of Deep Renovation Packages Based on Industrialized Solutions: Definition and Application," Sustainability, MDPI, vol. 13(11), pages 1-18, June.
    14. Anna Borawska & Mariusz Borawski & Małgorzata Łatuszyńska, 2022. "Effectiveness of Electricity-Saving Communication Campaigns: Neurophysiological Approach," Energies, MDPI, vol. 15(4), pages 1-19, February.
    15. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    16. Mithila Seva Bala Sundaram & ChiaKwang Tan & Jeyraj Selvaraj & Ab. Halim Abu Bakar, 2023. "Energy Savings for Various Residential Appliances and Distribution Networks in a Malaysian Scenario," Energies, MDPI, vol. 16(13), pages 1-18, June.
    17. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    18. Gianluca Trotta & Kirsten Gram-Hanssen & Pernille Lykke Jørgensen, 2020. "Heterogeneity of Electricity Consumption Patterns in Vulnerable Households," Energies, MDPI, vol. 13(18), pages 1-17, September.
    19. McKenna, R. & Hofmann, L. & Merkel, E. & Fichtner, W. & Strachan, N., 2016. "Analysing socioeconomic diversity and scaling effects on residential electricity load profiles in the context of low carbon technology uptake," Energy Policy, Elsevier, vol. 97(C), pages 13-26.
    20. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(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:jsusta:v:13:y:2021:i:23:p:13258-:d:691699. 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.