"Optimizing Energy Storage Systems Using Supervised Machine Learning to Improve Renewable Energy Integration"

EasyChair Preprint 14452

Abstract

The integration of renewable energy sources (RES) into the power grid poses significant challenges due to their inherent intermittency and variability. To address these challenges, energy storage systems (ESS) play a crucial role in stabilizing the grid by balancing supply and demand. However, the optimization of ESS for enhanced efficiency, reliability, and cost-effectiveness remains a critical research area. This study investigates the application of supervised machine learning (ML) techniques to optimize ESS performance, aiming to improve the integration of renewable energy into the grid.

The research explores various supervised learning algorithms, including regression models, decision trees, and neural networks, to predict energy storage requirements and optimize the operational parameters of ESS. These models are trained using historical data on energy production, consumption patterns, weather conditions, and storage system performance. The study also examines the impact of different ML models on energy forecasting accuracy and the operational efficiency of ESS.

This study's findings demonstrate the potential of supervised ML in enhancing ESS optimization, leading to improved renewable energy integration. By reducing reliance on non-renewable energy sources and minimizing grid instability, the proposed approach contributes to the advancement of sustainable energy management practices. The research also highlights the economic benefits of ML-driven ESS optimization, including reduced operational costs and increased return on investment, positioning it as a viable solution for the future of renewable energy integration.

Keyphrases: Charge/Discharge Efficiency, ESS Optimization, Energy Forecasting, Energy Storage Systems (ESS), Renewable Energy Sources (RES), Supervised Machine Learning, grid stability, renewable energy integration

@booklet{EasyChair:14452,
  author    = {Kayode Sheriffdeen and Toluwani Bolu},
  title     = {"Optimizing Energy Storage Systems Using Supervised Machine Learning to Improve Renewable Energy Integration"},
  howpublished = {EasyChair Preprint 14452},
  year      = {EasyChair, 2024}}