IoT-Based Household Energy Demand Prediction Using Machine Learning Techniques

In this paper, a smart household energy demand prediction system based on IoT and Machine Learning has been proposed and implemented. The system uses voltage and current sensors connected to ESP32 to measure real-time electrical parameters. The collected data is stored in Firebase cloud database and further processed using a Python Flask application. Different machine learning algorithms were applied to predict future energy demand based on historical consumption data. From the experimental results, it was observed that advanced models like LSTM provide better accuracy compared to traditional regression methods. The system achieved approximately 92–95% prediction accuracy, which shows that the proposed approach is effective for household energy forecasting. The developed framework is low-cost, scalable, and suitable for smart home applications. It helps users to monitor their energy usage and plan their electricity consumption more efficiently. By predicting future demand, the system can also help in reducing peak load and electricity wastage. Overall, the integration of IoT, cloud computing, and machine learning provides an intelligent solution for modern residential energy management.

1.     Ou Ali, I. H., Agga, A., Ouassaid, M., Maaroufi, M., Elrashidi, A., & Kotb, H. (2024). Predicting Short-Term Energy Usage in a Smart Home Using Hybrid Deep Learning Models. Frontiers in Energy Research, 12. DOI: 10.3389/fenrg.2024.1323357. 2.     Nimbe, P., Acheampong, M. B., Osei, M., & Adu Brempong, C. (2026). Machine Learning-Based Prediction of Household Energy Consumption in Ghana. Research Square. DOI: 10.21203/rs.3.rs-7836277/v1. 3.     Jain, N., Sharma, S., Thakur, V., Nutakki, M., & Mandava, S. (2025). Prediction and Analysis of Household Energy Consumption Integrated with Renewable Energy Sources using ML Algorithms. International Journal of Renewable Energy Research, 15(1), 1-15. 4.     Liang, Z., & Chen, J. (2025). Research on Building Energy Consumption Prediction Algorithm Based on Customized Deep Learning Model. Energy Informatics, 8, 25. 5.     Neshat, M., Thilakaratne, M., El-Abd, M., Mirjalili, S., Gandomi, A. H., & Boland, J. (2025). Smart Buildings Energy Consumption Forecasting using Adaptive Evolutionary Ensemble Learning Models. arXiv. 6.     “Short-Term Energy Consumption Forecasting Analysis Using Neural Networks.” (2025). Applied Sciences, 15(12), 6839. 7.     Natarajan, Y., Preethaa, S. K. R., Wadhwa, G., Choi, Y., Chen, Z., Lee, D. E., & Mi, Y. (2024). Enhancing Building Energy Efficiency with IoT-Driven Hybrid Deep Learning Models. Sustainability, 16(5), 1925. 8.     Hymas, K. (2025). Improving Household Energy Consumption Prediction with ML and Real-World Data. The Science Archive. 9.     Rehman, S. U., & Iqbal, N. (2025). Electricity Consumption Prediction in Smart Buildings Using DL Approaches. Energy Informatics, 8, 131. 10.  Machine Learning Based Energy Demand Prediction. (2023). Energy Reports.