Multi-Horizon Machine Learning Model for Home Energy Consumption Prediction Considering Appliances Usage
References
- Y. Zhao, J. Li, C. Chen, and Q. Guan, “A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting,” Energies, vol. 18, no. 3, pp. 1–21, 2025, doi: 10.3390/en18030486.
- S. R. Vangipuram and A. V. Giridhar, “Short Term Residential Load Forecasting Using Temporal Weather Based Embedding Stacked LSTMs,” IEEE Latin America Transactions, vol. 23, no. 6, pp. 497–507, 2025, doi: 10.1109/TLA.2025.11007195.
- A. Bhandary, V. Dobariya, G. Yenduri, R. H. Jhaveri, S. Gochhait, and F. Benedetto, “Enhancing Household Energy Consumption Predictions Through Explainable AI Frameworks,” IEEE Access, vol. 12, no. March, pp. 36764–36777, 2024, doi: 10.1109/ACCESS.2024.3373552.
- M. Razghandi and D. Turgut, “Residential appliance-level load forecasting with deep learning,” in Proceedings - IEEE Global Communications Conference, GLOBECOM, Taipei, 2020, pp. 1–6. doi: 10.1109/GLOBECOM42002.2020.9348197.
- Y. Zhou, A. S. Nair, D. Ganger, A. Tripathi, C. Baone, and H. Zhu, “Appliance Level Short-term Load Forecasting via Recurrent Neural Network,” in IEEE Power and Energy Society General Meeting, Denver, 2022, pp. 1–5. doi: 10.1109/PESGM48719.2022.9917171.
- Z. Severiche-Maury, C. Uc-Ríos, J. E. Sierra, and A. Guerrero, “Predicting Energy Consumption and Time of Use of Home Appliances in an HEMS Using LSTM Networks and Smart Meters: A Case Study in Sincelejo, Colombia,” Sustainability (Switzerland), vol. 17, no. 11, 2025, doi: 10.3390/su17114749.
- F. Wahid, R. Ghazali, L. H. Ismail, and A. M. A. Aseere, “An Optimal Neural Network for Hourly and Daily Energy Consumption Prediction in Buildings,” International Journal of Swarm Intelligence Research, vol. 14, no. 1, pp. 1–13, 2023, doi: 10.4018/IJSIR.316649.
- H. Tantyoko, S. R. Purnama, and E. Vianita, “Multi-Horizon Short-Term Residential Load Forecasting Using Decomposition-Based Linear Neural Network,” Advance Sustainable Science, Engineering and Technology, vol. 7, no. 3, pp. 1–8, 2025, doi: 10.26877/asset.v7i3.2033.
- L. Ismail, H. Materwala, and F. K. Dankar, “Machine Learning Data-Driven Residential Load Multi-Level Forecasting with Univariate and Multivariate Time Series Models Toward Sustainable Smart Homes,” IEEE Access, vol. 12, no. April, pp. 55632–55668, 2024, doi: 10.1109/ACCESS.2024.3383958.
- B. Devanathan, J. J. Varshitha, P. P. Kumar, S. A. Lakshmanan, and K. K. Prakash, “Explainable AI Framework Using XGBoost With SHAP and LIME for Multi-Scale Household Energy Forecasting,” IEEE Access, vol. 13, no. August, pp. 149750–149764, 2025, doi: 10.1109/ACCESS.2025.3602673.
- F. Lazzari et al., “User behaviour models to forecast electricity consumption of residential customers based on smart metering data,” Energy Reports, vol. 8, pp. 3680–3691, 2022, doi: 10.1016/j.egyr.2022.02.260.
- K. Liang, F. Liu, and Y. Zhang, “Household Power Consumption Prediction Method Based on Selective Ensemble Learning,” IEEE Access, vol. 8, pp. 95657–95666, 2020, doi: 10.1109/ACCESS.2020.2996260.
- S. Iram et al., “An Innovative Machine Learning Technique for the Prediction of Weather Based Smart Home Energy Consumption,” IEEE Access, vol. 11, no. July, pp. 76300–76320, 2023, doi: 10.1109/ACCESS.2023.3287145.
- Z. Severiche-Maury et al., “LSTM Networks for Home Energy Efficiency,” Designs, vol. 8, no. 4, pp. 1–19, 2024, doi: 10.3390/designs8040078.
- A. Pai H, K. K. Mishra, M. T. R, J. V. M. L. Jeyan, and A. Sayal, “Enhanced household energy consumption forecasting using multivariate long short-term memory (LSTM) networks with weather data integration,” Results in Engineering, vol. 27, no. April, p. 106512, 2025, doi: 10.1016/j.rineng.2025.106512.
- L. Fan, J. Li, and X. P. Zhang, “Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition,” CSEE Journal of Power and Energy Systems, vol. 6, no. 3, pp. 563–571, 2020, doi: 10.17775/CSEEJPES.2018.01130.
- A. Mehmood, K. T. Lee, and D. H. Kim, “Energy Prediction and Optimization for Smart Homes with Weather Metric-Weight Coefficients,” Sensors, vol. 23, no. 7, pp. 1–22, 2023, doi: 10.3390/s23073640.
- R. Li et al., “MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting,” Computers, Materials and Continua, vol. 82, no. 2, pp. 2969–2984, 2025, doi: 10.32604/cmc.2025.060230.
- Q. W. Khan, R. Ahmad, A. Rizwan, A. N. Khan, K. T. Lee, and D. H. Kim, “Optimizing energy efficiency and comfort in smart homes through predictive optimization: A case study with indoor environmental parameter consideration,” Energy Reports, vol. 11, no. October 2023, pp. 5619–5637, 2024, doi: 10.1016/j.egyr.2024.05.038.
- T. Singh, A. Solanki, S. K. Sharma, N. Z. Jhanjhi, and R. M. Ghoniem, “Grey Wolf Optimization-Based CNN-LSTM Network for the Prediction of Energy Consumption in Smart Home Environment,” IEEE Access, vol. 11, no. October, pp. 114917–114935, 2023, doi: 10.1109/ACCESS.2023.3311751.
- A. Binbusayyis and M. Sha, “Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism,” Heliyon, vol. 11, no. 1, p. e41507, 2025, doi: 10.1016/j.heliyon.2024.e41507.
- K. H. Kumar Reddy, R. K. Behera, M. H. Gururaj, and R. K. Bailayar Singh, “An Ensemble Learning based Energy Forecasting Model: A Sustainable Home Energy Management System for Smart City,” Procedia Computer Science, vol. 258, pp. 1316–1325, 2025, doi: 10.1016/j.procs.2025.04.365.
- K. H. Baesmat, E. E. Regentova, and Y. Baghzouz, “A Hybrid machine learning–statistical based method for short-term energy consumption prediction in residential buildings,” Energy and AI, vol. 21, no. July, p. 100552, 2025, doi: 10.1016/j.egyai.2025.100552.
- S. Priyanto, A. Soetedjo, and I. B. Sulistiawati, “Monitoring the Power Consumption of Home Appliances Using an IoT-Based SCADA System,” in 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024, Yogyakarta, Indonesia: IEEE, 2024, pp. 441–446. doi: 10.1109/ICITISEE63424.2024.10730065.
- S. Priyanto, A. Soetedjo, and I. B. Sulistiawati, “Integration of SCADA and embedded fuzzy-based load scheduling in Home Energy Management System with grid-connected PV,” INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, vol. 18, no. 1, pp. 1–24, 2025, doi: 10.2478/ijssis-2025-0020.
- L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.
- A. Cutler, D. R. Cutler, and J. R. Stevens, “Random Forests,” in Ensemble Machine Learning, C. Zhang and Y. Ma, Eds., Boston, MA: Springer, 2012, pp. 157–175. doi: 10.1007/978-1-4419-9326-7.
- J. H. Friedman, “GREEDY FUNCTION APPROXIMATION: A GRADIENT BOOSTING MACHINE,” The Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001.
- G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree,” in Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, 2017, pp. 3146–3154.
- T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 785–794. doi: 10.1145/2939672.2939785.
- A. Ibrahem Ahmed Osman, A. Najah Ahmed, M. F. Chow, Y. Feng Huang, and A. El-Shafie, “Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia,” Ain Shams Engineering Journal, vol. 12, no. 2, pp. 1545–1556, 2021, doi: 10.1016/j.asej.2020.11.011.
- W. Dong, Y. Huang, B. Lehane, and G. Ma, “XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring,” Automation in Construction, vol. 114, p. 103155, Jun. 2020, doi: 10.1016/j.autcon.2020.103155.
- J. L. Elman, “Finding structure in time,” Cognitive Science, vol. 14, no. 2, pp. 179–211, 1990, doi: 10.1016/0364-0213(90)90002-E.
- S. Hochreiter and J. Schmidhuber, “Lstm,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1007/978-3-030-63416-2_300359.
- J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling,” arXiv preprint arXiv:1412.3555, 2014, [Online]. Available:
http://arxiv.org/abs/1412.3555 - “scikit-learn: Machine Learning in Python.” Accessed: Oct. 01, 2024. [Online]. Available:
https://scikit-learn.org/stable/ - “Light Gradient Boosting Machine.” Accessed: Jul. 01, 2025. [Online]. Available:
https://github.com/microsoft/LightGBM - “eXtreme Gradient Boosting.” Accessed: Jul. 01, 2025. [Online]. Available:
https://github.com/dmlc/xgboost?tab=readme-ov-file - “TensorFlow: An end-to-end platform for machine learning.” Accessed: Jul. 01, 2025. [Online]. Available:
https://www.tensorflow.org/
DOI: https://doi.org/10.2478/ijssis-2026-0038 | Journal eISSN: 1178-5608
Language: English
Submitted on: Dec 6, 2025
Published on: Jul 11, 2026
Published by: International Journal on Smart Sensing and Intelligent Systems
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© 2026 Irrine Budi Sulistiawati, Aryuanto Soetedjo, Irmalia Suryani Faradisa, published by International Journal on Smart Sensing and Intelligent Systems
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