A Framework for Modeling and Optimization of Data-Driven Energy Systems Using Machine Learning

This article introduces an innovative framework for solar energy optimization. This approach delves into the multifaceted layers and components of neural networks (NNs), elucidating their complexities and interconnections. The proposed framework strategically combines tailored algorithms and processes to address the optimization problem. The methodology ultimately leads to selecting an intelligent model that guarantees superior performance, accuracy, and adaptability across a broad spectrum of interdisciplinary applications. It offers valuable insights to researchers and practitioners striving to leverage NNs for complex data-driven solutions. Furthermore, this study introduces an online simulation tool powered by a new algorithm to monitor the optimal generation capacity of solar systems. This tool employs a uniquely designed optimization model architecture to track alterations in system output in response to environmental and input variable changes. This study presents a mathematical model of the system in Python to enhance accessibility and adaptability, allowing for easy adoption across various applications. This work is a straightforward reference, bridging the gap between research-oriented and tutorial methodologies.