Optimizing Solar Energy Production in the USA: Time-Series Analysis Using AI for Smart Energy Management

TL;DR

This paper demonstrates that AI models like Random Forest and XG-Boost can accurately forecast solar energy production across the US, enabling smarter grid management and supporting renewable energy policies.

Contribution

It introduces the application of AI time-series models for solar energy forecasting in the US, showing their high accuracy and potential for real-time grid optimization.

Findings

Random Forest and XG-Boost models achieved high accuracy in solar energy prediction.

Both models demonstrated comparable and reliable performance across diverse datasets.

AI forecasting can enhance grid stability and support decarbonization efforts.

Abstract

As the US rapidly moves towards cleaner energy sources, solar energy is fast becoming the pillar of its renewable energy mix. Even while solar energy is increasingly being used, its variability is a key hindrance to grid stability, storage efficiency, and system stability overall. Solar energy has emerged as one of the fastest-growing renewable energy sources in the United States, adding noticeably to the country's energy mix. Retrospectively, the necessity of inserting the sun's energy into the grid without disrupting reliability and cost efficiencies highlights the necessity of good forecasting software and smart control systems. The dataset utilized for this research project comprised both hourly and daily solar energy production records collected from multiple utility-scale solar farms across diverse U.S. regions, including California, Texas, and Arizona. Training and evaluation of all models were performed with a time-based cross-validation scheme, namely, sliding window validation. Both the Random Forest and the XG-Boost models demonstrated noticeably greater and the same performance across each of the measures considered, with relatively high accuracy. The almost perfect and equal performance by the Random Forest and XG-Boost models also shows both models to have learned the patterns in the data very comprehensively, with high reliability in their predictions. By incorporating AI-powered time-series models like XG-Boost in grid management software, utility companies can dynamically modify storage cycles in real-time as well as dispatch and peak load planning, based on their predictions. AI-powered solar forecasting also has profound implications for renewable energy policy and planning, particularly as U.S. federal and state governments accelerate toward ambitious decarbonization goals.