Optimizing Machine Learning Algorithms for Predicting and Reducing Thermal Waste in Grid-Connected Renewable Energy Conversion Systems

The rapid deployment of grid-connected renewable energy conversion systems (R-ECS) such as photovoltaic (PV), PV–thermal (PV/T), hybrid configurations, and microgrids has heightened concerns about thermal waste generation and its implications for efficiency, reliability, and environmental impact. This review surveys the state-of-the-art in machine learning (ML) and deep learning (DL) driven strategies for predicting, mitigating, and ultimately reducing thermal waste in grid-connected renewables, with emphasis on control and optimization perspectives. The objective is to synthesize how algorithmic design, feature engineering, and hybrid ML–control frameworks can model the complex thermal–electrical coupling, predict transient thermal profiles, and enable proactive thermal management through adaptive control, retuning, and load/dispatch decisions. The methodologies surveyed cover data-driven thermal modeling, ML-based prognostics, optimization of thermal pathways, and integration with grid dynamics, including PV and PV/T collectors, thermal storage, and microgrid operation under varying irradiance, ambient conditions, and electrical load. Findings indicate that ML models trained with physics-informed features and hybrid architectures, augmented by real-time data streams from PV arrays, inverters, and thermal channels, can capture nonlinear thermo-electrical interactions more accurately than traditional physics-based models alone. Hybrid approaches that couple ML predictors with controller ensembles or model-predictive control (MPC) frameworks show promise in minimizing thermal waste through proactive curtailment, heat extraction optimization, and dynamic reconfiguration of thermal routes. Key challenges include data scarcity for rare extreme events, transferability across sites, interpretability of DL models, and balancing thermal performance with electrical efficiency and grid stability. The review identifies critical gaps in standardized benchmarks, uncertainty quantification, and scalable deployment in fielded grid-connected R-ECS.

کلیدواژه ها:

Thermal Waste ، Machine Learning ، Grid-Connected Renewables ، Control ، Optimization