This study proposes an integrated framework for predicting household electricity consumption and solar photovoltaic (PV) generation by combining user behavior and weather data. Meteorological data from BMKG Banjarbaru and household survey data were utilized, incorporating behavioral variables such as appliance usage frequency, watt meter capacity, and household characteristics. A comparative analysis was conducted using multiple models, including Linear Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree Regression (DTR), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). In addition, a hybrid CNN–LSTM model was developed to enhance prediction performance for solar PV generation. Model evaluation was performed using MAE, RMSE, and MAPE under different data split scenarios (70:30, 80:20, and 90:10). The results show that the hybrid CNN–LSTM model achieves consistent and accurate performance in predicting solar PV generation, while Linear Regression provides stable and interpretable results for household energy consumption. From a behavioral perspective, electricity usage is primarily influenced by usage related factors rather than demographic characteristics. This study contributes by integrating demand side and supply side prediction into a unified framework for energy management. The findings reveal that energy supply is more predictable due to environmental factors, while energy demand is strongly influenced by user behavior. This integrated perspective provides practical insights for improving decision-making and supporting sustainable energy management.
Abd Aziz, M. A. S., & Ahmad, N. (2024). Economic Impacts of Performance Optimization in Large-Scale Solar Farms: A Case Study Using Artificial Neural Networks in Eastern Malaysia. International Journal of Academic Research in Economics and Management Sciences, 13(3). https://doi.org/10.6007/ijarems/v13-i3/22258
Al-Haj Hussein, M. I. M., Awang, Z., & Al Nohoud, O. M. (2025). Regulatory Mediation as a Policy Lever in Renewable Energy Deployment: Evidence from the Jordanian Solar Sector. International Journal of Academic Research in Business and Social Sciences, 15(4). https://doi.org/10.6007/ijarbss/v15-i4/25382
Almughram, O., Zafar, B., & Slama, S. Ben. (2022). Home Energy Management Machine Learning Prediction Algorithms: A Review.
Azhar, A. H. A. A., Mohamad, R., Suliman, S. I., Kassim, M., & Abdul Rahman, F. Y. (2022). Development of a Solar-Powered Car Ventilation System with Wireless Monitoring. International Journal of Academic Research in Business and Social Sciences, 12(6). https://doi.org/10.6007/ijarbss/v12-i6/13990
Bagdadee, A. H., Rahman, M. S., Al Mamoon, I., Dewi, D. A., Muzahidul Islam, A. K. M., & Zhang, L. (2025). Empowering smart homes by IoT-driven hybrid renewable energy integration for enhanced efficiency. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-25328-2
Caminiti, C. M., Merlo, M., Fotouhi Ghazvini, M. A., & Edvinsson, J. (2024). optimHome: A Shrinking Horizon Control Architecture for Bidirectional Smart Charging in Home Energy Management Systems. Energies, 17(8). https://doi.org/10.3390/en17081963
Deng, X., Da, F., Shao, H., & Wang, X. (2023). A Survey of the Researches on Grid-Connected Solar Power Generation Systems and Power Forecasting Methods Based on Ground-Based Cloud Atlas. In Energy Engineering: Journal of the Association of Energy Engineering (Vol. 120, Number 2, pp. 385–408). Tech Science Press. https://doi.org/10.32604/ee.2023.023480
Dernouni, M., Bouchekima, B., Necib, D., Arab, A., & Ben Kheridla, F. (2024). Investigation of the potential for electrification of remote areas using parabolic solar collectors in southern Algeria. Heliyon, 10(7). https://doi.org/10.1016/j.heliyon.2024.e29264
Hadzaman, N. A. H., Syed Muzzafar Shah, S. N. A., Ismam, J. N., & Talkis, N. A. (2022). Solar Panel Mechanical Cleaning Systems in Commercial Buildings. International Journal of Academic Research in Business and Social Sciences, 12(11). https://doi.org/10.6007/ijarbss/v12-i11/15134
Ikhsan, M. R., Lakulu, M. M., Pannesai, I. Y., Rizali, M., Nugraha, B., & Swastina, L. (2026). Systematic review of artificial intelligence applications in predicting solar photovoltaic power production efficiency. International Journal of Electrical and Computer Engineering (IJECE), 16(1), 463. https://doi.org/10.11591/ijece.v16i1.pp463-476
Ingo, T. I., Gyoh, L., Sheng, Y., Kaymak, M. K., ?ahin, A. D., & Pouran, H. M. (2024). Accelerating the Low-Carbon Energy Transition in Sub-Saharan Africa through Floating Photovoltaic Solar Farms. Atmosphere, 15(6). https://doi.org/10.3390/atmos15060653
Jalal, M., Rehman, A. U., Haq, A. U., & Khalil, I. U. (2024). Estimation of Domestic Load Profile for Effective Demand-Side Management. Engineering World, 6, 100–105. https://doi.org/10.37394/232025.2024.6.10
Jamil, A. S., Abd Aziz, M. A. S., Annuar, I., & Ahmad, N. (2025). Techno-Economic Assessment of Solar Farm Output Optimization Using Artificial Neural Networks (ANN): A Case Study from Pahang, Malaysia. International Journal of Academic Research in Economics and Management Sciences, 14(4). https://doi.org/10.6007/ijarems/v14-i4/26869
Koukaras, P., Mustapha, A., Mystakidis, A., & Tjortjis, C. (2024). Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models. Energies, 17(6). https://doi.org/10.3390/en17061450
Mahmood, I., Arshad Nasir, H., Javed, F., & Aguado, J. A. (2020). A Hierarchical Multi-Resolution Agent Based Modeling and Simulation Framework for Household Electricity Demand Profile. https://doi.org/10.1177/0037549720923401
Mustaqeem, Ishaq, M., & Kwon, S. (2021). Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach. IEEE Access, 9, 94262–94271. https://doi.org/10.1109/ACCESS.2021.3093053
Nacht, T., Pratter, R., Ganglbauer, J., Schibline, A., Aguayo, A., Fragkos, P., & Zisarou, E. (2023). Modeling Approaches for Residential Energy Consumption: A Literature Review. In Climate (Vol. 11, Number 9). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/cli11090184
Proedrou, E. (2021). A Comprehensive Review of Residential Electricity Load Profile Models. In IEEE Access (Vol. 9, pp. 12114–12133). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2021.3050074
Rahman, R., Teknik Elektro, P., Kalimantan, I., & Banjarmasin, M. (2021). Analisis Perencanaan Pembangkit Listrik Tenaga Surya Offgrid Untuk Rumah Tinggal Di Kota Banjarbaru. https://doi.org/10.31602/eeict.v4i1.4540
Razavi, S. E., Arefi, A., Ledwich, G., Nourbakhsh, G., Smith, D. B., & Minakshi, M. (2020). From Load to Net Energy Forecasting: Short-Term Residential Forecasting for the Blend of Load and PV behind the Meter. IEEE Access, 8, 224343–224353. https://doi.org/10.1109/ACCESS.2020.3044307
Sayed, M. (2024). Bottom-Up Medium-Term Hourly Resolution Household Electricity Load Profile Model. https://doi.org/10.21203/rs.3.rs-4561990/v1
Sievers, J., & Blank, T. (2023). A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems. In Energies (Vol. 16, Number 4). MDPI. https://doi.org/10.3390/en16041688
Sulaiman, N. A., Abdul Rahman, M. S., Md Yusop, A., Abdullah, M. P., Khafe, A. M., & Sulaiman, S. F. (2024). Solar Powered Lighting System for Educational Institutions: Assessing Efficiency and Sustainability. International Journal of Academic Research in Business and Social Sciences, 14(12). https://doi.org/10.6007/ijarbss/v14-i12/24411
Sunder, R., R, S., Paul, V., Punia, S. K., Konduri, B., Nabilal, K. V., Lilhore, U. K., Lohani, T. K., Ghith, E., & Tlija, M. (2024). An advanced hybrid deep learning model for accurate energy load prediction in smart building. Energy Exploration and Exploitation, 42(6), 2241–2269. https://doi.org/10.1177/01445987241267822
Talib, M. M., & Croock, M. S. (2023). AI-Enhanced Power Management System for Buildings: A Review and Suggestions. Journal Europeen Des Systemes Automatises, 56(3), 383–391. https://doi.org/10.18280/jesa.560304
Taweekul, K. (2020). Farmer’s Economic Change after Participating Vegetable Cultivation Using Solar Cell Supported by Private Sector. International Journal of Academic Research in Business and Social Sciences, 10(11). https://doi.org/10.6007/ijarbss/v10-i11/8182
Xie, C., Wang, D., Lai, C. S., Wu, R., Wu, X., & Lai, L. L. (2021). Optimal sizing of battery energy storage system in smart microgrid considering virtual energy storage system and high photovoltaic penetration. Journal of Cleaner Production, 281, 125308. https://doi.org/10.1016/j.jclepro.2020.125308
Xie, Y., & Mohd Noor, A. I. (2022). Factors Affecting Residential End-Use Energy: Multiple Regression Analysis Based on Buildings, Households, Lifestyles, and Equipment. Buildings, 12(5). https://doi.org/10.3390/buildings12050538
Ye, X., Zhang, Z., & Qiu, Y. (Lucy). (2023). Review of application of high frequency smart meter data in energy economics and policy research. Frontiers in Sustainable Energy Policy, 2. https://doi.org/10.3389/fsuep.2023.1171093
Ikhsan, M. R., Lakulu, M. M. Bin, Pannesai, I. Y., Rizali, M., & Byna, A. (2026). Integrated Prediction of Household Energy Consumption and Solar PV Generation Using Linear Regression and Hybrid CNN–LSTM. International Journal of Academic Research in Business and Social Sciences, 16(5), 918–940.
Copyright: © 2026 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
