Review of the Advancement in Artificial Intelligence and Machine Learning in Sustainable Energy Using Solar-Biomass Hybrid Energy Systems
Downloads
This study investigates the integration of artificial intelligence (AI) and machine learning (ML) technologies in solar-biomass hybrid energy systems for sustainable energy generation. Through a comprehensive empirical analysis of 250 hybrid energy installations across five regions, this research evaluates the performance optimization potential of AI/ML algorithms in renewable energy management. The study employed a mixed-methods approach with quantitative data collection from operational systems over 24 months (2023-2024). Results demonstrate that AI-enhanced solar-biomass systems achieved 34.7% higher energy efficiency compared to conventional systems, with ML-based predictive maintenance reducing operational costs by 28.3%. The study found significant correlations between AI implementation and system reliability (r=0.847, p<0.001), while deep learning models improved energy forecasting accuracy to 92.4%. Integration challenges were identified in 18.2% of installations, primarily related to data synchronization and algorithm compatibility. These findings contribute to advancing sustainable energy technologies through intelligent optimization frameworks, providing empirical evidence for the transformative potential of AI/ML in hybrid renewable energy systems.
Abdelmoula, I. A., Oufettoul, H., Lamrini, N., Motahhir, S., Mehdary, A., & El, A. M. (2024). Federated learning for solar energy applications: A case study on real-time fault detection. Solar Energy, 282, 112942.
Adu, D., Jianguo, D., Asomani, S. N., & Abbey, A. (2024). Energy generation and carbon dioxide emission—The role of renewable energy for green development. Energy Reports, 12, 1420–1430.
Ahmad, E., Khan, D., Anser, M. K., Nassani, A. A., Hassan, S. A., & Zaman, K. (2024). The influence of grid connectivity, electricity pricing, policy-driven power incentives, and carbon emissions on renewable energy adoption: Exploring key factors. Renewable Energy, 232, 121108.
Alam, M. M., Hossain, M. J., Zamee, M. A., & Al-Durra, A. (2025). Design and operation of future low-voltage community microgrids: An AI-based approach with real case study. Applied Energy, 377, 124523.
Aljohani, A. (2024). Deep learning-based optimization of energy utilization in IoT-enabled smart cities: A pathway to sustainable development. Energy Reports, 12, 2946–2957.
Alsaiari, A. O., Moustafa, E. B., Alhumade, H., Abulkhair, H., & Elsheikh, A. (2023). A coupled artificial neural network with artificial rabbits optimizer for predicting water productivity of different designs of solar stills. Advances in Engineering Software, 175, 103315.
AlShafeey, M., & Csaki, C. (2024). Adaptive machine learning for forecasting in wind energy: A dynamic, multi-algorithmic approach for short and long-term predictions. Heliyon, 10, e34807.
Álvarez-Arroyo, C., Vergine, S., de la Nieta, A. S., Alvarado-Barrios, L., & D'Amico, G. (2024). Optimising microgrid energy management: Leveraging flexible storage systems and full integration of renewable energy sources. Renewable Energy, 229, 120701.
An, J., Yeom, S., Hong, T., Jeong, K., Lee, J., Eardley, S., Clifford, M., & Leach, M. (2024). Analysis of the impact of energy consumption data visualization using augmented reality on energy consumption and indoor environment quality. Building and Environment, 250, 111177.
Araoye, T. O., Ashigwuike, E. C., Mbunwe, M. J., Bakinson, O. I., & Ozue, T. G. I. (2024). Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm. Renewable Energy, 229, 120712.
Arroyo, Á., Basurto, N., Casado-Vara, R., Timiraos, M., & Calvo-Rolle, J. L. (2024). A hybrid intelligent modeling approach for predicting the solar thermal panel energy production. Neurocomputing, 565, 126997.
Assareh, E., Keykhah, A., Bedakhanian, A., Agarwal, N., & Lee, M. (2025). Optimizing solar photovoltaic farm-based cogeneration systems with artificial intelligence (AI) and cascade compressed air energy storage for stable power generation and peak shaving: A Japan-focused case study. Applied Energy, 377, 124468.
Babay, M.-A., Adar, M., Chebak, A., & Mabrouki, M. (2025). Forecasting green hydrogen production: An assessment of renewable energy systems using deep learning and statistical methods. Fuel, 381, 133496.
Bakare, M. S., Abdulkarim, A., Shuaibu, A. N., & Muhamad, M. M. (2024). Predictive energy control for grid-connected industrial PV-battery systems using GEP-ANFIS. e-Prime - Advances in Electrical Engineering, Electronics and Energy, 9, 100647.
Balachandran, G. B., Devisridhivyadharshini, M., Ramachandran, M. E., & Santhiya, R. (2024). Comparative investigation of imaging techniques, pre-processing and visual fault diagnosis using artificial intelligence models for solar photovoltaic system—A comprehensive review. Measurement, 232, 114683.
Bamisile, O., Cai, D., Adun, H., Dagbasi, M., Ukwuoma, C. C., Huang, Q., Anoh, K., & Okafor, V. A. (2024). Towards renewables development: Review of optimization techniques for energy storage and hybrid renewable energy systems. Heliyon, 10, e37482.
Bao, M., Arzaghi, E., Abaei, M. M., Abbassi, R., Garaniya, V., Abdussamie, N., & Penesis, I. (2024). Site selection for offshore renewable energy platforms: A multi-criteria decision-making approach. Renewable Energy, 229, 120768.
Bennagi, A., AlHousrya, O., Cotfas, D. T., & Cotfas, P. A. (2024). Comprehensive study of the artificial intelligence applied in renewable energy. Energy Strategy Reviews, 54, 101446.
Biswal, B., Deb, S., Datta, S., Ustun, T. S., & Cali, U. (2024). Review on smart grid load forecasting for smart energy management using machine learning and deep learning techniques. Energy Reports, 12, 3654–3670.
Cao, T., Xu, Y., Liu, G., Tao, S., Tang, W., & Sun, H. (2024). Feature-enhanced deep learning method for electric vehicle charging demand probabilistic forecasting of charging station. Applied Energy, 371, 123751.
Carnevale, D., Cavaiola, M., & Mazzino, A. (2024). A novel AI-assisted forecasting strategy reveals the energy imbalance sign for the day-ahead electricity market. Energy Reports, 11, 4115–4126.
Chae, B., Sheu, C., & Park, E. O. (2024). The value of data, machine learning, and deep learning in restaurant demand forecasting: Insights and lessons learned from a large restaurant chain. Decision Support Systems, 184, 114291.
Chen, Y., Li, H., Xu, Y., Fu, Q., Wang, Y., He, B., Yang, Z., Liu, W., & Zhang, C. (2024). Sustainable management in irrigation water distribution system under climate change: Process-driven optimization modelling considering water-food-energy-environment synergies. Agricultural Water Management, 302, 108990.
Danish, M. S. S., & Senjyu, T. (2023). Shaping the future of sustainable energy through AI-enabled circular economy policies. Circular Economy, 2, 100040.
Dong, W., Chen, X., & Yang, Q. (2022). Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability. Applied Energy, 308, 118387.
Durgun, Y. (2024). Real-time water quality monitoring using AI-enabled sensors: Detection of contaminants and UV disinfection analysis in smart urban water systems. Journal of King Saud University - Science, 36, 103409.
Ejiyi, C. J., Cai, D., Ejiyi, M. B., Chikwendu, I. A., Coker, K., Oluwasanmi, A., Ugwu, C., & Asere, S. K. (2024). Polynomial-SHAP analysis of liver disease markers for capturing of complex feature interactions in machine learning models. Computers in Biology and Medicine, 182, 109168.
El Maghraoui, A., El Hadraoui, H., Ledmaoui, Y., El Bazi, N., Guennouni, N., & Chebak, A. (2024). Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability. Sustainable Energy, Grids and Networks, 39, 101452.
Elizabeth Michael, N., Hasan, S., Al-Durra, A., & Mishra, M. (2022). Short-term solar irradiance forecasting based on a novel Bayesian optimized deep long short-term memory neural network. Applied Energy, 324, 119727.
Elkholy, M. H., Elymany, M., Yona, A., Senjyu, T., Takahashi, H., & Elsayed, L. M. (2023). Experimental validation of an AI-embedded FPGA-based real-time smart energy management system using multi-objective reptile search algorithm and gorilla troops optimizer. Energy Conversion and Management, 282, 116860.
Fadoul, F. F., Hassan, A. A., & Çağlar, R. (2024). Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti. Results in Engineering, 24, 103033.
Fang, X., Zhong, X., Dong, W., Zhang, F., & Yang, Q. (2024). Fuzzy logic-based coordinated operation strategy for an off-grid photovoltaic hydrogen production system with battery/supercapacitor hybrid energy storage. International Journal of Hydrogen Energy, 84, 593–605.
Faruque, M. O., Hossain, M. A., Islam, M. R., Alam, S. M. M., & Karmaker, A. K. (2024). Very short-term wind power forecasting for real-time operation using hybrid deep learning model with optimization algorithm. Clean Energy Systems, 9, 100129.
Ghandourah, E., Prasanna, Y. S., Elsheikh, A. H., Moustafa, E. B., Fujii, M., & Deshmukh, S. S. (2023). Performance prediction of aluminum and polycarbonate solar stills with air cavity using an optimized neural network model by golden jackal optimizer. Case Studies in Thermal Engineering, 47, 103055.
Ghenai, C., Ahmad, F. F., & Rejeb, O. (2024). Artificial neural network-based models for short term forecasting of solar PV power output and battery state of charge of solar electric vehicle charging station. Case Studies in Thermal Engineering, 61, 105152.
Ghimire, S., Nguyen-Huy, T., Deo, R. C., Casillas-Pérez, D., & Salcedo-Sanz, S. (2022). Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model. Sustainable Materials and Technologies, 32, e00429.
Ghimire, S., Nguyen-Huy, T., Prasad, R., Deo, R. C., Casillas-Pérez, D., Salcedo-Sanz, S., & Kayser, B. (2023). Hybrid convolutional neural network-multilayer perceptron model for solar radiation prediction. Cognitive Computation, 15, 645–671.
Gomathi, S., Kannan, E., Carmel Mary Belinda, M. J., Giri, J., Nagaraju, V., Aravind Kumar, J., & Sathyamurthy, R. (2024). Solar energy prediction with synergistic adversarial energy forecasting system (Solar-SAFS): Harnessing advanced hybrid techniques. Case Studies in Thermal Engineering, 63, 105197.
Guo, F., Woo, H. S., Kim, D., & Moon, H. J. (2025). Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building. Applied Energy, 377, 124467.
Hajimirza Amin, N., Etemad, A., & Abdalisousan, A. (2024). Data-driven performance analysis of an active chilled beam air conditioning system: A machine learning approach for energy efficiency and predictive maintenance. Results in Engineering, 23, 102747.
Hanif, M. F., & Mi, J. (2024). Harnessing AI for solar energy: Emergence of transformer models. Applied Energy, 369, 123541.
Hanifi, S., Cammarono, A., & Zare-Behtash, H. (2024). Advanced hyperparameter optimization of deep learning models for wind power prediction. Renewable Energy, 221, 119700.
Hassan, Q., Algburi, S., Sameen, A. Z., Salman, H. M., & Jaszczur, M. (2023). A review of hybrid renewable energy systems: Solar and wind-powered solutions: Challenges, opportunities, and policy implications. Results in Engineering, 20, 101621.
He, Y., Guo, S., Dong, P., Lv, D., & Zhou, J. (2023). Feasibility analysis of decarbonizing coal-fired power plants with 100% renewable energy and flexible green hydrogen production. Energy Conversion and Management, 290, 117232.
Heidarykiany, R., & Ababei, C. (2024). HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning. Energy and AI, 16, 100362.
Hu, C., Li, D., Zhao, W., & Xi, H. (2024). Deep reinforcement learning-based scheduling for integrated energy system utilizing retired electric vehicle battery energy storage. Journal of Energy Storage, 97, 112774.
Hu, J., Lin, Y., Li, J., Hou, Z., Chu, L., Zhao, D., & Liu, F. (2024). Performance analysis of AI-based energy management in electric vehicles: A case study on classic reinforcement learning. Energy Conversion and Management, 300, 117964.
Huang, X., Li, Q., Tai, Y., Chen, Z., Zhang, J., Shi, J., Gao, B., & Huang, Y. (2021). Hybrid deep neural model for hourly solar irradiance forecasting. Renewable Energy, 171, 1041–1060.
Ibrahim, O., Abdul Aziz, M. J., Ayop, R., Dahiru, A. T., Low, W. Y., Sulaiman, M. H., Tan, C. K., & Alkahtani, A. A. (2024). Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation. Results in Engineering, 24, 102816.
Işık, G., Öğüt, H., & Mutlu, M. (2023). Deep learning based electricity demand forecasting to minimize the cost of energy imbalance: A real case application with some Fortune 500 companies in Türkiye. Engineering Applications of Artificial Intelligence, 118, 105664.
