Photovoltaic (PV) systems have become one of the most promising alternative energy sources, as they transform the sun’s energy into electricity. This can frequently be achieved without causing any potential harm to the environment. Although their usage in residential places and building sectors has notably increased, PV systems are regarded as unpredictable, changeable, and irregular power sources. This is because, in line with the system’s geographic region, the power output depends to a certain extent on the atmospheric environment, which can vary drastically. Therefore, artificial intelligence (AI)-based approaches are extensively employed to examine the effects of climate change on solar power. Then, the most optimal AI algorithm is used to predict the generated power. In this study, we used machine learning (ML)-based algorithms to predict the generated power of a PV system for residential buildings. Using a PV system, Pyranometers, and weather station data amassed from a station at King Khalid University, Abha (Saudi Arabia) with a residential setting, we conducted several experiments to evaluate the predictability of various well-known ML algorithms from the generated power. A backward feature-elimination technique was applied to find the most relevant set of features. Among all the ML prediction models used in the work, the deep-learning-based model provided the minimum errors with the minimum set of features (approximately seven features). When the feature set is greater than ten features, the polynomial regression model shows the best prediction, with minimal errors. Comparing all the prediction models, the highest errors were associated with the linear regression model. In general, it was observed that with a small number of features, the prediction models could minimize the generated power prediction’s mean squared error value to approximately 0.15.

This paper introduces a novel integration of Grey Wolf Optimization (GWO) and Crow Search Algorithm (CSA) within an IoT-enabled framework to enhance the operational efficiency of Solar Photovoltaic (PV) Energy Systems. The primary focus is on maximizing the power output through an advanced Maximum Power Point Tracking (MPPT) system. The proposed GWO-CSA algorithm synergizes the explorative capabilities of the Grey Wolf Optimization and the exploitative strengths of the Crow Search Algorithm, leading to a robust and efficient tracking mechanism. The implementation of IoT technology facilitates real-time monitoring and adaptive control, further optimizing the system's performance. Simulation results, conducted under varying environmental conditions, demonstrate a significant improvement in efficiency and stability over traditional MPPT methods. The GWO-CSA based system shows remarkable adaptability to fluctuating solar irradiance and temperature, ensuring optimal power extraction even under suboptimal conditions. This enhancement in operational efficiency not only increases the energy yield but also extends the lifespan of the solar panels. This research contributes to the field of renewable energy by providing a scalable and effective solution for maximizing solar energy utilization. The integration of IoT and advanced optimization algorithms presents a promising avenue for future developments in sustainable energy systems.