Harnessing AI Neural Networks and Generative AI for Optimized Solar Energy Production and Residential Battery Storage Management

Abstract

The rapid rise of residential solar photovoltaic (PV) and battery storage systems is transforming energy grids worldwide into decentralized, sustainable systems that face the challenge of providing energy when it is most needed among households and communities rather than when it is readily available. This transformation involves an escalating number of distributed resources and changing household demand patterns, thus involving sophisticated optimization problems that limit the large-scale deployment of these energy systems. This research explores and presents a comprehensive pyramidal approach utilizing different artificial intelligence neural networks in tandem with optimization to address the optimal operation of rooftop solar PV, residential battery storage, and household loads. Machine learning, including an artificial neural network regression model and a long-short term memory model, is presented to forecast solar generation and household demand, which then outline the input to a groundbreaking optimization problem to adapt the operation schedule of battery systems. This work showcases the potential of modern AI strategies and provides a robust approach that can be implemented in utilities or for individual households. The use of AI systems and large-scale data analysis is key to the safe and proactive operation of this emerging infrastructure. The AI systems, including cutting-edge advances like Generative Adversarial Networks (GANs), are particularly important in managing the increasing scale of distributed renewable energy and increasing battery storage uptake. This research has revealed that a novel and adaptive action needs to be taken every forecast period in response to changing environmental factors and lays important foundations for integrated management strategies regarding solar production, smart battery storage, and personalized smart loads.