Harnessing AI Neural Networks and Generative AI for Optimized Solar Energy Production and Residential Battery Storage Management
Keywords:
Generative AI, machine learning, AI, operation, solar power, data analysis, optimization, energy supply, demand, battery storage, electricity trading, consumer, net load, time series analysis, neural network, forecasting, solar energy generation, residential electricity demand, convolutional neural network, LSTM, GAN.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.
