Data-Driven Cloud Systems for Renewable Energy Optimization

Yousif, Hayder Abdulameer
Hussain, Salah Yehia
Hassan Ali, Taif Sami
Al-Doori, Vian S.
Sabah, Ahmed
Batumalay, M.

International Journal of Engineering, Science and Information Technology
Vol 5, No 2 (2025)

The growing share of renewable generation in global power systems creates operational instability due to the volatile nature of solar, wind, and hydropower. This study presents a novel cloud-edge integrated model designed to enhance the performance and efficiency of these renewable sources through a data-centric approach. The proposed architecture relies on an IoT-enabled sensor network for real-time data gathering, processed through a hybrid infrastructure combining edge-level filtration with cloud-based analytics. For energy output prediction, we compared Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) models, with LSTM demonstrating superior performance. To optimize operations, a multi-objective Genetic Algorithm was implemented to simultaneously minimize energy losses and costs while improving grid utilization balance. Furthermore, exergy-based modeling was employed to evaluate the thermodynamic quality of energy transformations. The results confirmed that the system significantly improved predictive accuracy, responsiveness, and energy savings. Under varying loads, the system maintained low latency and high energy allocation efficiency, validating its real-time adaptability. In summary, this research delivers a modular and scalable solution for intelligent energy management, highlighting the power of predictive analytics and adaptive control in creating data-driven, next-generation sustainable energy efficiency systems.