Optimalisasi Sistem Monitoring Dan Prediksi Konsumsi Energi Berbasis IoT Dan Deep Learning Untuk Smart Building Di Kota Palembang

Fadli; Tata Sutabri

doi:10.61722/jinu.v3i1.7901

Authors

Fadli Universitas Bina Darma
Tata Sutabri Universitas Bina Darma

DOI:

https://doi.org/10.61722/jinu.v3i1.7901

Keywords:

Deep Learning,IoT,LSTM,,Node-RED,Smart Bulding

Abstract

The increase in urbanization and commercial infrastructure development in Palembang City drives a surge in electrical energy demand. Energy efficiency is crucial, yet conventional reactive building management systems often fail to anticipate waste. This research proposes an integrated Smart Building system combining Internet of Things (IoT) based on Node-RED for real-time monitoring and Deep Learning using Long Short-Term Memory (LSTM) algorithm to predict energy consumption. Simulation dataset was collected for 90 days at 5-minute intervals, covering electrical and environmental parameters. Experimental results show that the proposed LSTM model can predict electricity load 1 hour ahead with high accuracy, achieving Mean Absolute Error (MAE) of 0.78 kW and Root Mean Square Error (RMSE) of 1.05 kW, outperforming ARIMA baseline statistical method. Prediction-based control strategy simulation shows potential energy savings of 8-12% through peak shaving techniques on air conditioning and lighting systems.

References

Al-Ali, A. R., Zualkernan, I. A., Rashid, M., Gupta, R., & Alikarar, M. (2017). A smart home energy management system using IoT and big data analytics approach. IEEE Transactions on Consumer Electronics, 63(4), 426–434. https://doi.org/10.1109/TCE.2017.015014

Avancini, D. B., Rodrigues, J. J. P. C., Martins, S. G. B., Rabêlo, R. A. L., Al-Muhtadi, J., & Solic, P. (2019). Energy meters evolution in smart grids: A review. Journal of Cleaner Production, 217, 702–715. https://doi.org/10.1016/j.jclepro.2019.01.229

Balaji, S., & Karthik, S. (2023). Deep Learning Based Energy Consumption Prediction on Internet of Things Environment. Intelligent Automation & Soft Computing, 37(1), 727–743. https://doi.org/10.32604/iasc.2023.037838

Barokah, M. R. S., & Sutabri, T. (2025). Pemanfaatan Sistem Cerdas Berbasis Internet of Things (IoT) untuk Optimalisasi Pengelolaan Energi di Smart Home. Jurnal Sains Studi dan Penelitian, 3(2), 422–425.

Chujai, H., Kerdprasop, N., & Kerdprasop, K. (2013). Time series forecasting of household electric consumption using ARIMA and cooperative neuro-evolution. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 1, pp. 295–300). Hong Kong.

Elsisi, M., Mahmoud, M., Lehtonen, M., & Darwish, M. M. F. (2021). Deep learning-based Industry 4.0 and Internet of Things towards effective energy management for smart buildings. Sensors, 21(4), 1038. https://doi.org/10.3390/s21041038

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y. (2019). Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid, 10(1), 841–851. https://doi.org/10.1109/TSG.2017.2753802

Lekić, M., & Gardašević, M. (2018). IoT sensor integration to Node-RED platform. In 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 1185–1190). Opatija, Croatia. https://doi.org/10.23919/MIPRO.2018.8400211

Plageras, O. B. P. R. P. L., Psannis, K. E., Stergiou, C., Wang, H., & Gupta, B. B. (2018). Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings. Future Generation Computer Systems, 82, 349–357. https://doi.org/10.1016/j.future.2017.09.082

Ramson, S. J., Vishnu, S., & Shanmugam, M. (2020). Applications of Node-RED in industrial IoT—A review. In 2020 International Conference on Communication and Signal Processing (ICCSP) (pp. 1086–1090). Chennai, India. https://doi.org/10.1109/ICCSP48568.2020.9182118

Reddy, S. S. (2021). Electrical load forecasting using hybrid ARIMA-LSTM algorithm. International Journal of Energy Research, 45(12), 18086–18099. https://doi.org/10.1002/er.6941

Zakiansyah, M., & Sutabri, T. (2025). Integrasi Internet of Things (IoT) dan Kecerdasan Buatan (AI) untuk Smart City di Indonesia. Jurnal Sains Studi dan Penelitian, 3(2), 346–354.