Weather-Informed Probabilistic Forecasting and Scenario Generation in Power Systems
This work addresses the problem of reliable renewable energy forecasting for power grid operators, representing an incremental improvement through hybrid modeling.
This paper tackles the challenge of forecasting renewable energy sources in power grids by proposing a weather-informed probabilistic forecasting method that combines Gaussian copula with Temporal Fusion Transformers. The results show superior performance with comprehensive metrics on real-world MISO data, demonstrating the importance of weather information and Gaussian copula for generating realistic scenarios.
The integration of renewable energy sources (RES) into power grids presents significant challenges due to their intrinsic stochasticity and uncertainty, necessitating the development of new techniques for reliable and efficient forecasting. This paper proposes a method combining probabilistic forecasting and Gaussian copula for day-ahead prediction and scenario generation of load, wind, and solar power in high-dimensional contexts. By incorporating weather covariates and restoring spatio-temporal correlations, the proposed method enhances the reliability of probabilistic forecasts in RES. Extensive numerical experiments compare the effectiveness of different time series models, with performance evaluated using comprehensive metrics on a real-world and high-dimensional dataset from Midcontinent Independent System Operator (MISO). The results highlight the importance of weather information and demonstrate the efficacy of the Gaussian copula in generating realistic scenarios, with the proposed weather-informed Temporal Fusion Transformer (WI-TFT) model showing superior performance.