Smart IoE: A Hybrid Machine Learning Framework for Wind Energy Forecasting, Blockchain Integrity, and IoE Connectivity
Mansoor Khan, Salabat Khan, Muhammad Asghar Khan, Ximeng Dou, Zahid Ali Khan, Feng Cao, Fahmida Bibi
Abstract. Wind energy is a renewable energy that can be used in a wide variety of applications. Incorporation of renewable energy sources into the power grid, which is wind energy, requires proper forecasting, effective resource allocation, and high data integrity systems. To solve these issues, a machine-learning framework created within the proposed structure will combine the best forecasting models with optimization features that are made possible through blockchain elements. Exact real-time forecasts of wind energy can be achieved by an integrated platform where deep neural networks are mixed with ensemble techniques and other machine-learning algorithms. The system provides the optimal allocation of all produced energy resources because of its inbuilt optimization facilities. This is because the introduction of blockchain in the architecture provides full data security, both in the energy and transactional integrity, which at the same time are highly transparent. The suggested structure enhances the accuracy of prediction at the phase of resources-distribution optimization and leads to the creation of a data-management subsystem, which is tamper-resistant. It also provides smart grids with sustainable wind-energy solutions by building entirely power-prediction systems together with distribution-management infrastructure and information-protection elements. Evaluation metrics substantiate the efficacy of the proposed framework. The hybrid model delivers optimal predictive accuracy, evidenced by a root mean square error (RMSE) of 1.25 and a mean absolute error (MAE) of 0.98. It also attains a coefficient of determination (R²) of 0.92, surpassing all benchmark ensembles, including Boosting Trees (RMSE: 1.35, R²: 0.90) and Random Forest (RMSE: 1.55, R²: 0.85).
Keywords
Hybrid Machine Learning, Wind Energy Forecasting, Resource Allocation, Renewable Energy Integration, Energy Grid Optimization
Published online 4/25/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Mansoor Khan, Salabat Khan, Muhammad Asghar Khan, Ximeng Dou, Zahid Ali Khan, Feng Cao, Fahmida Bibi, Smart IoE: A Hybrid Machine Learning Framework for Wind Energy Forecasting, Blockchain Integrity, and IoE Connectivity, Materials Research Proceedings, Vol. 64, pp 797-804, 2026
DOI: https://doi.org/10.21741/9781644904091-99
The article was published as article 99 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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