AN INTELLIGENT MACHINE LEARNING FRAMEWORK FOR RENEWABLE ENERGY GENERATION FORECASTING AND RESOURCE-AWARE ENERGY MANAGEMENT
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Assistant Professor
Cape Institute of Technology
Levenjipuram
Cape Institute of Technology
Levenjipuram
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Abstract
The increasing integration of renewable power consumption sources into modern power platforms creates notable challenges in accurately predicting power consumption generation and efficiently managing available power, since renewable performance measure is highly dependent on variable environmental conditions for example solar irradiance, temperature, wind speed, and humidity, as well as historical generation patterns. This paper proposes an Intelligent Machine Learning Framework for Renewable Power consumption Generation Estimated performance measure and Power consumption Management that performs input information pre-processing, characteristic extraction and identification, and machine-learning-based forecasting of power consumption generation. Several machine learning and deep learning computational models are considered to learn the complicated relationships between environmental parameters and renewable power consumption performance measure. The predicted generation is then passed to an intelligent energy-management module that supports computationally resource-aware power allocation among load, battery storage, and grid, with the goal of reducing power wastage and strengthening utilization. Framework performance is assessed adopting Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The presented methodology targets precise renewable-power consumption forecasting combined with intelligent decision-making for smart grids, micro grids, and sustainable power consumption platforms, contributing to optimized grid stability, computationally resource-aware utilization, and consistent renewable-energy integration.
Keywords
Renewable Power consumption; Machine Learning; Power consumption Estimated performance measure; Power consumption Forecasting; Intelligent Power consumption Management; Smart Grid; Deep Learning; Sustainable Power consumption.
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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 10
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Oct 15 2026
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B.Muthukkumaran, (2026). An Intelligent Machine Learning Framework for Renewable Energy Generation Forecasting and Resource-Aware Energy Management. International Journal of Science, Strategic Management and Technology, 02(10), 1-9. https://doi.org/10.55041/ijsmt.v2i10.077
B.Muthukkumaran, . "An Intelligent Machine Learning Framework for Renewable Energy Generation Forecasting and Resource-Aware Energy Management." International Journal of Science, Strategic Management and Technology, vol. 02, no. 10, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i10.077.
B.Muthukkumaran, . "An Intelligent Machine Learning Framework for Renewable Energy Generation Forecasting and Resource-Aware Energy Management." International Journal of Science, Strategic Management and Technology 02, no. 10 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i10.077.
References
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[4] S. Shringi, L. M. Saini, and S. K. Aggarwal, "A review of data-driven deep learning models for solar and wind energy forecasting," Renewable Energy Focus, vol. 55, p. 100739, 2025.
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[10] B. O. Abisoye et al., "A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights," Renewable Energy Focus, 2024.
[2] A. Mosavi, A. Bahmani, and S. Ardabili, "Deep learning for renewable energy forecasting: A taxonomy, and systematic literature review," Journal of Cleaner Production, vol. 384, p. 135414, 2023.
[3] M. R. Ahmed, S. K. Saha, and M. A. Hossain, "A review and taxonomy of wind and solar energy forecasting methods based on deep learning," Energy and AI, vol. 4, p. 100060, 2021.
[4] S. Shringi, L. M. Saini, and S. K. Aggarwal, "A review of data-driven deep learning models for solar and wind energy forecasting," Renewable Energy Focus, vol. 55, p. 100739, 2025.
[5] O. Khouili, M. Hanine, M. Louzazni, M. A. López Flores, E. García Villena, and I. Ashraf, "Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review," Energy Strategy Reviews, vol. 59, p. 101735, 2025.
[6] U. K. Das, K. S. Tey, M. Seyedmahmoudian, S. Mekhilef, M. Y. I. Idris, W. Van Deventer, B. Horan, and A. Stojcevski, "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, 2018.
[7] H. Liu, C. Chen, X. Lv, X. Wu, and M. Liu, "Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods," Energy Conversion and Management, 2019.
[8] A. Bansal, "Sizing and forecasting techniques in photovoltaic-wind based hybrid renewable energy system: A review," Journal of Cleaner Production, 2022.
[9] N. Krishnan et al., "How solar radiation forecasting impacts the utilization of solar energy: A critical review," Journal of Cleaner Production, 2023.
[10] B. O. Abisoye et al., "A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights," Renewable Energy Focus, 2024.
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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