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Abstract

Abstract. Electricity consumption continues to rise alongside population growth and infrastructure development. Unfortunately, the majority of energy consumption is still reliant on fossil fuels. Commercial buildings and residences are major contributors to electricity consumption, accounting for 63.04% of the total usage. In the healthcare sector, clinics and hospitals also require significant electrical energy, contributing to a 21% rise in national electricity demand. Additionally, occupants' behavior influences electricity consumption by 30%. Faced with these challenges, the use of renewable energy, such as solar power, holds great potential for providing sustainable energy. This research aims to simulate energy consumption to aid in the selection of solar panel technology, considering occupant behavior. By understanding accurate electricity consumption, solar panels that meet the needs can be chosen to ensure long-term sustainability. Simulation of electricity consumption using dynamic system methods is performed to acquire daily electricity consumption data, a critical criterion in solar panel selection. The study's results indicate that user behavior in utilizing electrical appliances significantly impacts overall energy consumption. The study implies the importance of understanding behavior to properly recognize actual electricity consumption.


Keywords: Energy consumption, simulation, system dynamic, user behaviour.

Keywords

System dynamics simulation Solar panel Energy consumption User behaviour

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Copyright @2017. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (http://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted non-commercial used, distribution and reproduction in any medium 

How to Cite
Firdaus, R., & Suryadi, K. (2024). Energy Consumption Model Based on User Behavior to Support Solar Panel Selection: Case Study of Dental Clinic . The Asian Journal of Technology Management (AJTM), 17(3), 193–208. https://doi.org/10.12695/ajtm.2024.17.3.4
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References

  1. Bawaneh, K., Ghazi Nezami, F., Rasheduzzaman, Md., & Deken, B., (2019). Energy Consumption Analysis and Characterization of Healthcare Facilities in the United States. Energies, 12(19).
  2. Chen, S., Zhang, G., Xia, X., Chen, Y., Setunge, S., & Shi, L., (2021). The impacts of occupant behavior on building energy consumption: A review. Sustainable Energy Technologies and Assessments, 45, 101212.
  3. De Bakker, C., de Voort, T., & Rosemann, A.,(2018). The Energy Saving Potential of Occupancy-Based Lighting Control Strategies in Open-Plan Offices: The Influence of Occupancy Patterns. Energies, 11(1).
  4. El-Bayeh, C. Z., Alzaareer, K., Brahmi, B., Zellagui, M., Eicker, U., (2021). An original multi-criteria decision-making algorithm for solar panels selection in buildings. Energy, 217, 119396.
  5. Ismail, M. S., Moghavvemi, M., & Mahlia, T. M. I., (2013). Analysis and evaluation of various aspects of solar radiation in the Palestinian territories. Energy Conversion and Management, 73, 57–68.
  6. Kim, Y. S., & Srebric, J., (2017). Impact of occupancy rates on the building electricity consumption in commercial buildings. Energy and Buildings, 138, 591–600.
  7. Kozlov, V., Sałabun, W., (2021). Challenges in reliable solar panel selection using MCDA methods. Procedia Computer Science, 192, 4913–4923.
  8. Lewis, C.D., (1982). International and Business Forecasting Methods. Butterworths, London.
  9. Mahdavi, A., Tahmasebi, F., & Kayalar, M., (2016). Prediction of plug loads in office buildings: Simplified and probabilistic methods. Energy and Buildings, 129, 322–329.
  10. Piselli, C., & Pisello, A. L., (2019). Occupant behavior long-term continuous monitoring integrated to prediction models: Impact on office building energy performance. Energy, 176, 667–681.
  11. Sasikumar, G., Ayyappan, S., (2019). Multi-criteria Decision Making for Solar Panel Selection Using Fuzzy Analytical Hierarchy Process and Technique for Order Preference by Similarity to ideal Solution (TOPSIS): An Empirical Study, Journal of The Institution of Engineers (India): Series C, 100 (4), 707–715.
  12. Seker, S., Kahraman, C., (2021). Socio-economic evaluation model for sustainable solar PV panels using a novel integrated MCDM methodology: A case in Turkey. Socio-Economic Planning Sciences, 77, 100998.
  13. Sekretariat Perusahaan PT. PLN (Persero), (2021). Statistik PLN 2020. Jakarta.
  14. Sekretaris Jenderal Dewan Energi Nasional, (2022). Outlook Energi Indonesia 2022. Jakarta.
  15. Shabbir, N., Usman, M., Jawad, M., Zafar, M. H., Iqbal, M. N., Kütt, L. (2020). Economic analysis and impact on national grid by domestic photovoltaic system installations in Pakistan, Renewable Energy, 153, 509–521.
  16. Sun, K., & Hon, T., (2017). A framework for quantifying the impact of occupant behavior on energy savings of energy conservation measures. Energy and Buildings, 146, 383–396.
  17. Sun, K., & Hon, T., (2017). A simulation approach to estimate energy savings potential of occupant behavior measures. Energy and Buildings, 136, 43–62.
  18. Tamosiunas, A., (2023). Selecting Rooftop Solar Photovoltaic Modules by Measuring Their Attractiveness by a Categorical-Based Evaluation Technique (MACBETH): The Case of Lithuania. Energies, 16 (7).
  19. Uddin, M. N., Chi, H. L., Wei, H. H., Lee, M., & Ni, M., (2022). Influence of interior layouts on occupant energy-saving behaviour in buildings: An integrated approach using Agent-Based Modelling, System Dynamics and Building Information Modelling. Renewable and Sustainable Energy Reviews, 161, 112382.
  20. Wu, Y., Xu, C., Ke, Y., Chen, K., Sun, X., (2018). An intuitionistic fuzzy multi-criteria framework for large-scale rooftop PV project portfolio selection: Case study in Zhejiang, China. Energy, 143, 295–309.