Benefits of spatio-temporal modelling for short term wind power forecasting at both individual and aggregated levels

TL;DR

This paper demonstrates that spatio-temporal models significantly improve short-term wind power forecasts at both individual and aggregated levels by leveraging correlations among wind farms, with proven effectiveness on real data from Denmark.

Contribution

It introduces a novel spatio-temporal modeling approach for wind power forecasting that enhances accuracy and enables spatially out-of-sample predictions, outperforming traditional autoregressive models.

Findings

Spatio-temporal models improve aggregated wind power forecasts.

Models enable accurate predictions at new, unobserved locations.

Spatio-temporal correlations are key to forecast accuracy.

Abstract

The share of wind energy in total installed power capacity has grown rapidly in recent years around the world. Producing accurate and reliable forecasts of wind power production, together with a quantification of the uncertainty, is essential to optimally integrate wind energy into power systems. We build spatio-temporal models for wind power generation and obtain full probabilistic forecasts from 15 minutes to 5 hours ahead. Detailed analysis of the forecast performances on the individual wind farms and aggregated wind power are provided. We show that it is possible to improve the results of forecasting aggregated wind power by utilizing spatio-temporal correlations among individual wind farms. Furthermore, spatio-temporal models have the advantage of being able to produce spatially out-of-sample forecasts. We evaluate the predictions on a data set from wind farms in western Denmark and compare the spatio-temporal model with an autoregressive model containing a common autoregressive parameter for all wind farms, identifying the specific cases when it is important to have a spatio-temporal model instead of a temporal one. This case study demonstrates that it is possible to obtain fast and accurate forecasts of wind power generation at wind farms where data is available, but also at a larger portfolio including wind farms at new locations. The results and the methodologies are relevant for wind power forecasts across the globe as well as for spatial-temporal modelling in general.