Predictive Modeling and Forecasting for Renewable Energy: Developing Innovative Data-Driven Models and Machine Learning Techniques

EasyChair Preprint no. 13815

Abstract

The rapid growth of renewable energy sources, such as solar and wind power, has led to an increased need for accurate and reliable forecasting of energy production. Traditional forecasting methods often fall short in capturing the inherent complexities and nonlinearities involved in renewable energy systems. This research paper presents the development of innovative data-driven models and machine learning techniques to improve the predictive modeling and forecasting of renewable energy generation.

The study combines historical data on weather patterns, solar irradiance, wind speed, and other relevant factors with advanced statistical and machine learning approaches. This includes the exploration of techniques such as time series analysis, neural networks, support vector machines, and ensemble methods. The models are designed to capture the dynamic relationships between environmental variables and renewable energy output, accounting for factors like seasonal variations, intermittency, and the impact of climate change.

Through extensive testing and validation on real-world renewable energy datasets, the research demonstrates significant improvements in forecasting accuracy and reliability compared to traditional forecasting methods. The developed models showcase the potential of data-driven approaches to enhance decision-making processes in renewable energy planning, grid integration, and market optimization.

The findings of this study contribute to the growing body of knowledge in the field of renewable energy forecasting and provide valuable insights for researchers, policymakers, and industry stakeholders. The innovative techniques presented can be further refined and adapted to address the unique challenges faced by different renewable energy systems worldwide.

Keyphrases: Energy, Renewable, systems

@Booklet{EasyChair:13815,
  author = {Dylan Stilinki and Klaus Hubert},
  title = {Predictive Modeling and Forecasting for Renewable Energy: Developing Innovative Data-Driven Models and Machine Learning Techniques},
  howpublished = {EasyChair Preprint no. 13815},

  year = {EasyChair, 2024}}