Abstract The significance of this study is rooted in the increasing dependence on solar energy as a primary source of renewable power. Solar energy facilities provide cost-effective operation, simple upkeep, and a high level of dependability, making them a compelling choice for the generation of clean energy. Nonetheless, the performance of these facilities can be greatly affected by outside elements such... The significance of this study is rooted in the increasing dependence on solar energy as a primary source of renewable power. Solar energy facilities provide cost-effective operation, simple upkeep, and a high level of dependability, making them a compelling choice for the generation of clean energy. Nonetheless, the performance of these facilities can be greatly affected by outside elements such as climate conditions and the physical attributes of the solar panels. This document explores various forecasting periods, from immediate to extended, and examines the appropriateness of various models, including artificial neural networks, time-series forecasting, machine learning, and combination approaches, for these purposes. By analyzing data from a solar energy facility, the research evaluates multiple regression models to determine the most accurate one. The Gradient Boosting Regressor was found to be the most effective, showcasing its capability in precisely predicting solar energy production. The success of this approach highlights the potential for more efficient integration of solar energy facilities into smart grid systems and the enhancement of energy management strategies. The study introduces a reliable method for real-time forecasting of solar energy facility performance, which could greatly improve energy management and the incorporation of renewable energy sources into power grids. It paves the way for further research on enhancing forecasting methods and emphasizes the importance of precise prediction models in the development of renewable energy technologies. Read More Read Less

Abstract This study explores how Long Short-Term Memory (LSTM) neural networks can be used as Maximum Power Point Tracking (MPPT) controllers for solar panels. It compares existing MPPT algorithms like Perturb and Observe (P&O), Incremental Conductance (IncCond), and Hill Climbing (HC) with LSTM-based methods in terms of their accuracy, efficiency, and ability to adapt. Data on voltage, current, power output, temperature,... This study explores how Long Short-Term Memory (LSTM) neural networks can be used as Maximum Power Point Tracking (MPPT) controllers for solar panels. It compares existing MPPT algorithms like Perturb and Observe (P&O), Incremental Conductance (IncCond), and Hill Climbing (HC) with LSTM-based methods in terms of their accuracy, efficiency, and ability to adapt. Data on voltage, current, power output, temperature, and solar irradiance from various locations are utilized to train and assess the LSTM model. The findings show that LSTM-based MPPT controllers are more effective than traditional methods, providing better tracking precision and the ability to adjust to changing environmental conditions. The research emphasizes the importance of LSTM-based controllers in improving the efficiency of solar panels and increasing the amount of energy collected. This study aids in the development of renewable energy technologies and highlights the role of artificial intelligence in optimizing solar energy systems. Read More Read Less

Abstract This research demonstrates how Artificial Intelligence (AI) can be used to keep military vehicles' engines running smoothly, with a special emphasis on Armored Fighting Vehicles. The transition from old-fashioned maintenance techniques to more forward-thinking predictive maintenance, powered by AI, is set to greatly improve the efficiency of military operations. By using sophisticated data analysis and sensor technologies, predictive maintenance allows... This research demonstrates how Artificial Intelligence (AI) can be used to keep military vehicles' engines running smoothly, with a special emphasis on Armored Fighting Vehicles. The transition from old-fashioned maintenance techniques to more forward-thinking predictive maintenance, powered by AI, is set to greatly improve the efficiency of military operations. By using sophisticated data analysis and sensor technologies, predictive maintenance allows for continuous monitoring of equipment, which in turn leads to better readiness and more efficient use of resources. The results show that using AI for predictive maintenance can transform the way Armored Fighting Vehicles are operated, make it easier to plan missions, and increase the overall efficiency of operations. These advantages, however, require addressing challenges such as collecting sensor data, setting up dependable communication networks, and ensuring the security of systems. Therefore, the introduction of AI offers a way to refine maintenance strategies, cut down on expenses, and guarantee optimal performance, thereby strengthening the readiness of troops and the effectiveness of the military as a whole. Read More Read Less