Abstract

The modern power systems are transformed due to the penetration of renewable energy sources (RESs) such as solar, wind, and hydrogen. Accurate forecasting of renewable generation, load demand, and system-level variables is crucial due to intermittency, nonlinearity, and strong weather dependency. It is important to ensure grid stability. Traditional time-series forecasting techniques used widely but shown limited capability in modeling complex nonlinear relationships and high-dimensional dependencies inherent in renewable energy data. The increasing availability of data from different sources like smart meters, weather stations, satellite imagery, and cyber-physical platforms shifted the current focus toward data-driven forecasting approaches. Artificial intelligence (AI) techniques like machine learning (ML), deep learning (DL), etc. demonstrated significant potential in data-driven forecasting. In this paper a brief review has been presented considering forecasting, optimization, and management techniques for the renewable energy systems. Forecasting models based on deep neural networks (DNNs), convolutional and recurrent architectures, transformers, ensemble learning, reinforcement learning (RL), and quantum-inspired optimization are systematically examined. Emphasis is also placed on hybrid and decomposition-based frameworks. Beyond predictive performance, this study highlights the growing integration of forecasting with optimization and control objectives, including cost minimization, energy efficiency improvement, battery lifetime extension, reliability enhancement, and carbon emission reduction. The scope, granularity, and realism of datasets employed in prior studies are also critically analyzed. The findings indicate that no optimal forecasting model exists; instead, superior performance is achieved through intelligent integration of learning, decomposition, optimization, and control strategies. The insights provided support the development of robust, scalable, and application-oriented artificial intelligence-based forecasting frameworks aligned with sustainable and resilient energy systems.

Keywords

Renewable energy forecasting, Artificial intelligence, Machine learning and Deep learning, Hybrid and ensemble models, Energy management and optimization, Power system stability.

Cite this article

Dubey AK. Advanced artificial intelligence techniques for forecasting, optimization, and management of renewable energy systems. Energy Intelligence and Sustainability. 2025;1(1):10-20. DOI : 10.19101/EIS.2025.11006

References

[1] Habyarimana M, Adebiyi AA. A review of artificial intelligence applications in predicting faults in electrical machines. Energies. 2025;18(7):1616.

[2] Vijay Kumar V, Shahin K. Artificial intelligence and machine learning for sustainable manufacturing: current trends and future prospects. Intelligent and Sustainable Manufacturing. 2025; 2(1):10002.

[3] Mallala B, Ahmed AI, Pamidi SV, Faruque MO, Reddy R. Forecasting global sustainable energy from renewable sources using random forest algorithm. Results in Engineering. 2025; 25:103789.

[4] Satpathy I, Nayak A, Jain V. The green city: Sustainable and smart urban living through artificial intelligence. In utilizing technology to manage territories 2025 (pp. 273-304). IGI Global.

[5] Sayeed F, Ahmed KR, Swamy SM. Artificial intelligence based succored power prediction in stand alone PV system. Australian Journal of Electrical and Electronics Engineering. 2025; 22(1):66-79.

[6] Hua J, Wang R, Hu Y, Chen Z, Chen L, Osman AI, et al. Artificial intelligence for calculating and predicting building carbon emissions: a review. Environmental Chemistry Letters. 2025:1-34.

[7] Zhou D, Liu Y, Wang X, Wang F, Jia Y. Combined ultra-short-term photovoltaic power prediction based on CEEMDAN decomposition and RIME optimized AM-TCN-BiLSTM. Energy. 2025; 318:134847.

[8] Al‐Raeei M. The smart future for sustainable development: Artificial intelligence solutions for sustainable urbanization. Sustainable Development. 2025; 33(1):508-17.

[9] Mirindi D, Khang A, Mirindi F. Artificial Intelligence (AI) and automation for driving green transportation systems: a comprehensive review. Driving Green Transportation System Through Artificial Intelligence and Automation: Approaches, Technologies and Applications. 2025:1-9.

[10] Adeoye YE, Onotole EF, Ogunyankinnu TU, Aipoh GO, Osunkanmibi AA, Egbemhenghe JO. Artificial intelligence in logistics and distribution: the function of AI in dynamic route planning for transportation including self-driving trucks and drone delivery systems. World Journal of Advanced Research and Reviews. 2025; 25(02):155-67.

[11] Zhao S, Chen S, Alsenani TR, Alotaibi B, Abuhussain M. Residential energy consumption and price forecasting in smart homes based on the internet of energy. Sustainable Energy Technologies and Assessments. 2025; 73:104081.

[12] Gohr C, Rodríguez G, Belomestnykh S, Berg-Moelleken D, Chauhan N, Engler JO, et al. Artificial intelligence in sustainable development research. Nature Sustainability. 2025; 8(8):970-8.

[13] Perveen G, Rizwan M, Goel N, Anand P. Artificial neural network models for global solar energy and photovoltaic power forecasting over India. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2025; 47(1):864-89.

[14] Infant SS, Vickram AS, Saravanan A, Muthu CM, Yuarajan D. Explainable artificial intelligence for sustainable urban water systems engineering. Results in Engineering. 2025:104349.

[15] Jamil U, Alva RJ, Ahmed S, Jin YF. Artificial intelligence-driven optimal charging strategy for electric vehicles and impacts on electric power grid. Electronics. 2025; 14(7):1471.

[16] Gul E, Baldinelli G, Wang J, Bartocci P, Shamim T. Artificial intelligence based forecasting and optimization model for concentrated solar power system with thermal energy storage. Applied Energy. 2025; 382:125210.

[17] Talaat FM, Kabeel AE, Shaban WM. Powering a sustainable future: AI-Driven integration of renewables for optimized grid management. Sustainable Futures. 2025: 100821.

[18] Shovon MS, Gomes CA, Reza SA, Bhowmik PK, Gomes CA, Jakir T, et al. Forecasting renewable energy trends in the USA: an AI-Driven analysis of electricity production by source. Journal of Ecohumanism. 2025; 4(3):322-45.

[19] Bhuiyan SM, Chowdhury A, Hossain MS, Mobin SM, Parvez I. AI-driven optimization in renewable hydrogen production: a review. American Journal of Interdisciplinary Studies. 2025; 6(1):76-94.

[20] Repetto M, Colapinto C, Tariq MU. Artificial intelligence driven demand forecasting: an application to the electricity market. Annals of Operations Research. 2025; 346(2):1637-51.

[21] Jadhav B, Kulkarni A, Khang A, Kulkarni P, Kulkarni S. Beyond the horizon: exploring the future of artificial intelligence (AI) powered sustainable mobility in public transportation system. In driving green transportation system through artificial intelligence and automation: approaches, technologies and applications 2025 (pp. 397-409). Cham: Springer Nature Switzerland.

[22] Bošnjaković M, Martinović M, Đokić K. Application of artificial intelligence in wind power systems. Applied Sciences. 2025; 15(5):2443.

[23] Saju L, Selvaraj D, Vairaperumal T. Artificial intelligence and machine intelligence: modeling and optimization of bioenergy production. In computer vision and machine intelligence for renewable energy systems 2025 (pp. 163-76). Elsevier.

[24] Khan AA, Laghari AA, Inam SA, Ullah S, Nadeem L. A review on artificial intelligence thermal fluids and the integration of energy conservation with blockchain technology. Discover Sustainability. 2025; 6(1):1-8.

[25] Rajaperumal TA, Columbus CC. Transforming the electrical grid: the role of AI in advancing smart, sustainable, and secure energy systems. Energy Informatics. 2025; 8(1):51.

[26] Sajjadi SS, Moghadassi A, Rashidizadeh-Kermani H, Kia R, Shafie-khah M. AI-driven forecasting for battery energy management in digital twin-integrated microgrids using machine learning techniques. Journal of Energy Storage. 2026; 143:119556.

[27] Rasouli A, Rastegar M. An ensemble of deep learning, machine learning, and statistical methods stacked with meta-learning for forecasting net energy consumption in multi-carrier energy systems: economic impact assessment. Energy. 2025:139172.

[28] Ghannam S. An explainable comparative study of statistical, machine learning, deep learning, and hybrid models for CO2 emissions forecasting in Australia. Array. 2025:100639.

[29] Otuka CI, Cai D, Ukwuoma CC, Luo S, Yang Z, Bamisile O, et al. Experimental review and recent advances in deep learning techniques for solar irradiance forecasting and prediction. Solar Energy. 2026; 304:114175.

[30] Rajaram G, Carmel MB, Bino J, Anish M, Sathish S, Malarkodi K, et al. Q-DASTNet: integrating quantum optimization and deep learning for robust solar energy forecasting in smart grid systems. Results in Engineering. 2025:107306.

[31] Xie Y, Ling X. Forecasting green hydrogen production in China: hybrid deep learning assessment of economic, environmental, and renewable energy integration. Renewable Energy. 2025:124414.

[32] Hsu YC, Hung YH, Lee CY. Robust ensemble forecasting and deep reinforcement learning for energy management on islanded microgrids. International Journal of Electrical Power & Energy Systems. 2025; 173:111405.

[33] Musbah H, Elsaraiti M. Forecasting load consumption: a comprehensive evaluation of deep learning and machine learning techniques. Electric Power Systems Research. 2025; 247:111834.

[34] Zohaib A, Akram F, Khalid S, Nawaz H, Rehman MU. Hybrid deep learning based load forecasting and AI-driven energy management for grid-connected multi-microgrids. Computers and Electrical Engineering. 2026; 131:110915.

[35] Kumar A, Singh AJ, Kumar S. Forecasting wind speed over multiple horizons: superiority of deep learning and decomposition-driven hybrid models. Unconventional Resources. 2025: 100296.

[36] Benti NE, Chaka MD, Semie AG. Forecasting renewable energy generation with machine learning and deep learning: current advances and future prospects. Sustainability. 2023; 15(9):7087.

[37] Rajasundrapandiyanleebanon T, Kumaresan K, Murugan S, Subathra MS, Sivakumar M. Solar energy forecasting using machine learning and deep learning techniques. Archives of Computational Methods in Engineering. 2023; 30(5):3059-79.

[38] Sri Revathi B. A survey on advanced machine learning and deep learning techniques assisting in renewable energy generation. Environmental Science and Pollution Research. 2023; 30:93407-21.

[39] Mirjalili MA, Aslani A, Zahedi R, Soleimani M. A comparative study of machine learning and deep learning methods for energy balance prediction in a hybrid building-renewable energy system. Sustainable Energy Research. 2023; 10(1):8.

[40] Elsaraiti M, Merabet A. Solar power forecasting using deep learning techniques. IEEE Access. 2022; 10:31692-8.

[41] Cordeiro-costas M, Villanueva D, Eguía-Oller P, Martínez-comesaña M, Ramos S. Load forecasting with machine learning and deep learning methods. Applied Sciences. 2023; 13(13):7933.

[42] Nadeem A, Hanif MF, Naveed MS, Hassan MT, Gul M, Husnain N, et al. AI-Driven precision in solar forecasting: breakthroughs in machine learning and deep learning. AIMS Geosciences. 2024;10(4):684-734.

[43] Radzi PN, Mekhilef S, Shah NM, Akhter MN, Seyedmahmoudian M, Almalaq Y, et al. Enhancing short-term solar power forecasting using a hybrid deep learning approach and a comparative analysis of machine learning models. Energy Conversion and Management: X. 2025:101431.