On autoregressive deep learning models for day-ahead wind power forecasting with irregular shutdowns due to redispatching

TL;DR

This paper compares autoregressive deep learning models and WP curve modeling for day-ahead wind power forecasting, highlighting challenges posed by irregular shutdowns and finding that WP curve models are more accurate and efficient.

Contribution

The study evaluates the impact of irregular redispatch shutdowns on forecasting methods and demonstrates the advantages of WP curve modeling over deep learning approaches in this context.

Findings

WP curve models outperform deep learning models in accuracy

WP curve models require less data cleaning and are more computationally efficient

Irregular shutdowns significantly affect forecasting accuracy

Abstract

Renewable energies and their operation are becoming increasingly vital for the stability of electrical power grids since conventional power plants are progressively being displaced, and their contribution to redispatch interventions is thereby diminishing. In order to consider renewable energies like Wind Power (WP) for such interventions as a substitute, day-ahead forecasts are necessary to communicate their availability for redispatch planning. In this context, automated and scalable forecasting models are required for the deployment to thousands of locally-distributed onshore WP turbines. Furthermore, the irregular interventions into the WP generation capabilities due to redispatch shutdowns pose challenges in the design and operation of WP forecasting models. Since state-of-the-art forecasting methods consider past WP generation values alongside day-ahead weather forecasts, redispatch shutdowns may impact the forecast. Therefore, the present paper highlights these challenges and analyzes state-of-the-art forecasting methods on data sets with both regular and irregular shutdowns. Specifically, we compare the forecasting accuracy of three autoregressive Deep Learning (DL) methods to methods based on WP curve modeling. Interestingly, the latter achieve lower forecasting errors, have fewer requirements for data cleaning during modeling and operation while being computationally more efficient, suggesting their advantages in practical applications.