Solar power continues to be a leading renewable energy source owing to its copious availability, scalability, and decreasing costs. Nevertheless, solar energy systems have several limitations in terms of their efficiency, dependability, and long-term sustainability.
The proposed hybrid solar energy system uses AI blends machine-learning-driven solar tracking, material upgrade with intelligence, adaptive photovoltaics, and energy management using blockchain into a common and intelligent platform for energy optimization.
By analyzing power generation data and employing advanced ML models, the research aims to enhance the efficiency and predictability of solar energy systems. The significance of this study lies in its potential to optimize renewable energy production, improve grid stability, and contribute to the transition towards sustainable energy sources.
In conclusion, the proposed X-LSTM-EO model, along with the use of the XAI-based LIME algorithm, offers a more accurate and transparent method for predicting solar power generation in solar plant systems. These findings have important implications for developing and deploying renewable energy sources, such as solar power.
The XAI-based Local Interpretable and Model-independent Explanation (LIME) is adapted to identify the critical factors that influence the accuracy of the power generation forecasts model in
Introduction Photovoltaic (PV) systems are expected to operate in varying conditions for at least 20 to 30 years, and the U.S. Department of Energy (DOE) supports research and
This work makes progress in this regard and presents a method for creating a real energy output model of photovoltaic systems in the form of an equivalent solar irradiance. The proposed
Though energy efficiency quantifies the instantaneous power generation, it does not embody the true operational output of a photovoltaic (PV) system, which brings exergy efficiency into
Therefore, precise solar power generation forecasting is necessary for a renewable energy system to operate effectively and economically. In this study, various machine learning
The PV module can be likened to an electrical system for energy conversion. This model has been widely studied in the literature [23 - 25] thanks to its relative simplicity.
Addressing the challenges of integrating photovoltaic (PV) systems into power grids, this research develops a dual-phase optimization model incorporating deep learning techniques.
By analyzing power generation data and employing advanced ML models, the research aims to enhance the efficiency and predictability of solar energy systems. The significance of this
Abstract The fusion of solar energy and artificial intelligence (AI) is revolutionizing how we visualize, optimize, and communicate renewable power systems. By 2025, AI-driven platforms like Reelmind.ai
The growing global demand for sustainable and clean energy has propelled international research into solar photovoltaic (PV) systems with more advanced designs. Solar power continues to
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