Learning from leading indicators to predict long-term dynamics of hourly electricity generation from multiple resources.

Abstract

Electricity is generated through various resources and then flows between regions via a complex system (grid). Imbalances in electricity generation can lead to the waste of renewable energy. As renewable energy is becoming a larger part of the grid, it is crucial to balance generation across different resources due to the instability of renewable energy production, which depends on climate conditions. Long-term forecasting of electricity generation from multiple resources and regions can help achieve the balance and create sufficient buffers for targeted adjustments. This study revisits the cross-correlation among various energy sources across regions. Certain time-series within the grid that exhibit early fluctuations are identified as leading indicators for others. Based on the utilization of leading indicators, ALI-GC is proposed for the comprehensive modelling of global energy source interactions. Additionally, a novel deep learning model, ALI-GRU, is proposed for long-term (up to a month) collaborative electricity generation forecasting. We obtained regional-level hourly electricity generation data for the entire U.S. spanning from 2018 to 2024. In the context of hourly end-to-end forecasting and online learning scenarios, our ALI-GRU consistently outperforms state-of-the-art models by up to 11.63%. Our work demonstrates strong adaptability in large-scale, real-time forecasting scenarios, providing practical benefits for improving renewable energy management and utilization practices.