Vol. 11 No. 12 (2025): December
Open Access
Peer Reviewed

Monitoring the Effectiveness of Energy Consumption for Old Buildings Using Data Gathered Through Temperature, Humidity, and Light Intensity

Authors

DOI:

10.29303/jppipa.v11i12.11706

Published:

2025-12-25

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Abstract

This research discusses the use of the Internet of Things in the monitoring of humidity, temperature, and light intensity conditions in a room that is connected to a mesh network. The objective of this research is to build a system that can monitor room conditions based on microcontrollers which are interconnected in a mesh network. The data are then displayed on a dashboard and categorized as either a comfortable or uncomfortable room based on existing standards. To ensure the accuracy of the system’s values, it is compared to commercial tools, then accuracy and precision are calculated. The system’s standard deviation for temperature is 0.12–0.19%, while its RMSE is 0.16–0.48%, and for humidity, the RMSE is 0.54–0.77%, with a standard deviation of 0.33–0.69%. For light intensity, with the outlier removed, the RMSE is 1.1–4.90% and the standard deviation is 0.79–2.76%. All these values are still comparable to the commercial tools’ accuracy listed in specification sheets. For packet loss, the system is run continuously for nine days, and at the end, the total data sent and data received at the server are calculated to count the differences. The packet loss after nine days and 777,600 data points is 0.00103–0.00193% from all six sensors used in the system.

Keywords:

Humidity IoT Mesh network SDG 11 SDG 13

References

Aquino, A. G. Q., Ballado, A. H., & Bautista, A. V. (2021). Implementing a Wireless Sensor Network with Multiple Arduino-Based Farming Multi-Sensor Tool to Monitor a Small Farm Area Using ESP32 Microcontroller Board. 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021. https://doi.org/10.1109/HNICEM54116.2021.9731989 DOI: https://doi.org/10.1109/HNICEM54116.2021.9731989

Awaludin, M., Rangan, A. Y., & Yusnita, A. (2021). Internet of Things (Iot) Based Temperature and Humidity Monitoring System in the Chemical Laboratory of the Samarinda Industry Standardization and Research Center. Tepian, 2(3), 85–93. https://doi.org/10.51967/tepian.v2i3.344 DOI: https://doi.org/10.51967/tepian.v2i3.344

Bertino, E., Jahanshahi, M. R., Singla, A., & Wu, R. T. (2021). Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap. Discover Internet of Things, 1(1). https://doi.org/10.1007/s43926-021-00009-4 DOI: https://doi.org/10.1007/s43926-021-00009-4

Carli, R., Cavone, G., Othman, S. Ben, & Dotoli, M. (2020). IoT based architecture for model predictive control of HVAC systems in smart buildings. Sensors (Switzerland), 20(3). https://doi.org/10.3390/s20030781 DOI: https://doi.org/10.3390/s20030781

Casals, M., Gangolells, M., Macarulla, M., Forcada, N., Fuertes, A., & Jones, R. V. (2020). Assessing the effectiveness of gamification in reducing domestic energy consumption: Lessons learned from the EnerGAware project. Energy and Buildings, 210. https://doi.org/10.1016/j.enbuild.2019.109753 DOI: https://doi.org/10.1016/j.enbuild.2019.109753

Filippidou, F., Nieboer, N., & Visscher, H. (2019). Effectiveness of energy renovations: a reassessment based on actual consumption savings. Energy Efficiency, 12(1). https://doi.org/10.1007/s12053-018-9634-8 DOI: https://doi.org/10.1007/s12053-018-9634-8

González-Torres, M., Pérez-Lombard, L., Coronel, J. F., Maestre, I. R., & Yan, D. (2022). A review on buildings energy information: Trends, end-uses, fuels and drivers. Energy Reports, 8, 626–637. https://doi.org/10.1016/j.egyr.2021.11.280 DOI: https://doi.org/10.1016/j.egyr.2021.11.280

Harb, H., Nader, D., Sabeh, K., & Makhoul, A. (2022). Real-time Approach for Decision Making in IoT-based Applications. Proceedings of the 11th International Conference on Sensor Networks, 223–230. https://doi.org/10.5220/0010985800003118 DOI: https://doi.org/10.5220/0010985800003118

Hong, W. Y., & Rahmat, B. N. N. N. (2022). Energy consumption, CO2 emissions and electricity costs of lighting for commercial buildings in Southeast Asia. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-18003-3 DOI: https://doi.org/10.1038/s41598-022-18003-3

Jiang, X., Zhang, H., Barsallo Yi, E. A., Raghunathan, N., Mousoulis, C., Chaterji, S., Peroulis, D., Shakouri, A., & Bagchi, S. (2021). Hybrid Low-Power Wide-Area Mesh Network for IoT Applications. IEEE Internet of Things Journal, 8(2), 901–915. https://doi.org/10.1109/JIOT.2020.3009228 DOI: https://doi.org/10.1109/JIOT.2020.3009228

Kim, D. B., Kim, D. D., & Kim, T. (2019). Energy performance assessment of HVAC commissioning using long-term monitoring data: A case study of the newly built office building in South Korea. Energy and Buildings, 204. https://doi.org/10.1016/j.enbuild.2019.109465 DOI: https://doi.org/10.1016/j.enbuild.2019.109465

Kulkarni, P. M., Parvekar, V., Nagpure, P., Mhoprekar, S., Mudawadkar, G., Nandurkar, S., & Hande, N. (2022). IOT Based Health Monitoring System. International Journal for Research in Applied Science and Engineering Technology, 10(12), 803–808. https://doi.org/10.22214/ijraset.2022.48022 DOI: https://doi.org/10.22214/ijraset.2022.48022

Kychkin, A. V., Gorshkov, O. V., Selivanov, V. A., & Pavlov, V. A. (2021). Technology for the soft implementing a digital twin into the IoT HVAC control loop. Journal Of Applied Informatics, 16(95). https://doi.org/10.37791/2687-0649-2021-16-5-33-47 DOI: https://doi.org/10.37791/2687-0649-2021-16-5-33-47

Laura, B., Andrea, V., Massimiliano, Z., & Riccardo, P. (2023). An investigation on humans’ sensitivity to environmental temperature. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-47880-5 DOI: https://doi.org/10.21203/rs.3.rs-3101216/v1

Liu, G. X., Shi, L. F., & Xin, D. J. (2020). Data Integrity Monitoring Method of Digital Sensors for Internet-of-Things Applications. IEEE Internet of Things Journal, 7(5), 4575–4584. https://doi.org/10.1109/JIOT.2020.2967504 DOI: https://doi.org/10.1109/JIOT.2020.2967504

Márquez, F. P. G. (2021). Introductory Chapter: Internet of Things. In Internet of Things. https://doi.org/10.5772/intechopen.98268 DOI: https://doi.org/10.5772/intechopen.98268

Mohd Ali, A., Dhimish, M., Alsmadi, M. M., & Mather, P. (2021). An Algorithmic Approach to Identify the Optimum Network Architecture and WLAN Protocol for VoIP Application. Wireless Personal Communications, 119(4). https://doi.org/10.1007/s11277-021-08383-6 DOI: https://doi.org/10.1007/s11277-021-08383-6

Rahman, R. A., Hashim, U. R. A., & Ahmad, S. (2020). IoT based temperature and humidity monitoring framework. Bulletin of Electrical Engineering and Informatics, 9(1), 229–237. Retrieved from https://www.beei.org/index.php/EEI/article/view/1557 DOI: https://doi.org/10.11591/eei.v9i1.1557

Sukmawati, E., Adhicandra, I., Sucahyo, N., Ayuningtyas, A., & Nurwijayanti, K. N. (2022). Information System Design of Online-Based Technology News Forum. International Journal Of Artificial Intelligence Research, 1(2). https://doi.org/10.29099/ijair.v6i1.2.593

Zhang, F., Saeed, N., & Sadeghian, P. (2023). Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis. In Energy and AI (Vol. 12). https://doi.org/10.1016/j.egyai.2023.100235 DOI: https://doi.org/10.1016/j.egyai.2023.100235

Zhao, D., Watari, D., Ozawa, Y., Taniguchi, I., Suzuki, T., Shimoda, Y., & Onoye, T. (2023). Data-driven online energy management framework for HVAC systems: An experimental study. Applied Energy, 352. https://doi.org/10.1016/j.apenergy.2023.121921 DOI: https://doi.org/10.1016/j.apenergy.2023.121921

Author Biographies

John Reigton Hartono, Bina Nusantara University

Author Origin : Indonesia

Ditdit Nugeraha Utama, Bina Nusantara University

Author Origin : Indonesia

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How to Cite

Hartono, J. R., & Utama, D. N. (2025). Monitoring the Effectiveness of Energy Consumption for Old Buildings Using Data Gathered Through Temperature, Humidity, and Light Intensity. Jurnal Penelitian Pendidikan IPA, 11(12), 149–159. https://doi.org/10.29303/jppipa.v11i12.11706