The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

Sensitivity to ambient air temperatures, consuming a large amount of fuel, and wasting a significant amount of heat dumped into the ambient atmosphere are three major challenges facing gas turbine power plants. This study was conducted to simultaneously solve all three aforementioned GT problems using solar energy and introducing a new configuration that consists of solar preheating and inlet-air-cooling systems. In this study, air was preheated at a combustion chamber inlet using parabolic trough collectors. Then, inlet air to the compressor was cooled by these collectors by operating an absorption cooling cycle. At the design point conditions, this novel proposed integration resulted in a 6.87% relative increase in generated power and a 10.53% relative decrement in fuel consumption, achieving a 19.45% relative increment in the plant’s thermal efficiency. This was accompanied by a reduction of 0.026 kg/s, 4.2 kg/s, and 0.278 kg/s in CO₂, CO, and NOx emissions, respectively. Finally, spider diagrams were employed to assess the impact of the operating parameters on the overall system’s performance and its associated environmental implications.