Hybrid renewable energy systems with photovoltaic and energy storage systems have gained popularity due to their cost-effectiveness, reduced dependence on fossil fuels and lower CO₂ emissions. However, their techno-economic advantages are crucially dependent on the optimal sizing of the system. Most of the commercially available optimization programs adopt an algorithm that assumes repeated weather conditions, which is becoming more unrealistic considering the recent erratic behavior of weather patterns. To address this issue, a data-driven framework is proposed that combines machine learning and hybrid metaheuristics to predict weather patterns over the lifespan of a hybrid renewable energy system in optimizing its size. The framework uses machine learning tree ensemble methods such as the cat boost regressor, light gradient boosting machine and extreme gradient boosting to predict the hourly solar radiation and load demand. Nine different hybrid metaheuristics are used to optimize the hybrid renewable energy system using forecasted data over 15 years, and the optimal sizing results are compared with those obtained from 1-year data simulation. The proposed approach leads to a more realistic hybrid renewable energy system capacity that satisfies all system constraints while being more reliable and environmentally friendly. The proposed framework provides a robust approach to optimizing hybrid renewable energy system sizing and performance evaluation that accounts for changing weather conditions over the lifespan of the system.

An ejector is a simple mechanical device that can be integrated with power generation or the refrigeration cycle to enhance their performance. Owing to the complex flow behavior in the ejector, the performance prediction of the ejector is done by numerical simulations. However, to evaluate the performance of an ejector integrated power cycle or refrigeration cycle, the need for simpler and more reliable thermodynamic models to estimate the performance of the ejector persists. This research, therefore, aims at developing a single mathematical correlation that can predict the ejector performance with reasonable accuracy. The proposed correlation relates the entrainment ratio and the pressure rise across the ejector to the area ratio and the mass flow rate of the primary flow. R141b is selected as the ejector refrigerant, and the results obtained through the proposed correlation are validated through numerical solutions. The comparison between the analytical and numerical with experimental results provided an error of less than 8.4% and 4.29%, respectively.