Harnessing Blockchain and Deep Learning for Enhanced Energy Analytics in Photovoltaic Systems

The rapid evolution of solar energy in industrial sectors has sparked a growing interest in renewable energy, particularly within the context of smart grids and systems. The effective detection and management of energies within photovoltaic (PV) systems stand as crucial tasks in this landscape. This abstract introduces a proposed model aimed at exploring the integration of blockchain networks within large-scale solar energy infrastructures. The primary objective is to establish a framework that enables the distribution of solar energy in a decentralized manner to end-users while ensuring secure energy transactions between producers and consumers. Blockchain technology serves as the cornerstone of this model, encompassing intelligent techniques to address energy mitigation challenges within the solar energy network. By leveraging blockchain, the detection of energies within solar power plants becomes shared knowledge accessible to all users connected to the solar power network. The research within this framework emphasizes the exploration and application of diverse Deep Learning (DL) architectures, including autoencoders, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Deep Stacking Networks (DSNs), and Deep Belief Networks (DBNs). Furthermore, this research extends beyond conventional machine learning approaches, incorporating knowledge-driven methodologies. It incorporates generative models such as adversarial networks and transfer learning, elevating the scope of machine learning in optimizing energy detection, distribution, and consumption within the block chain-based solar energy infrastructure. This interdisciplinary approach signifies a significant stride in fortifying renewable energy systems. By amalgamating blockchain's decentralized nature with sophisticated DL architectures and knowledge-driven models, it establishes a resilient and efficient renewable energy infrastructure capable of meeting escalating demands for sustainable energy solutions.