A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Benefits of spatiotemporal modeling for short‐term wind power forecasting at both individual and aggregated levels
The share of wind energy in total installed power capacity has grown rapidly in recent years. 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 spatiotemporal models for wind power generation and obtain full probabilistic forecasts from 15 min to 5 h ahead. Detailed analyses of forecast performances on individual wind farms and aggregated wind power are provided. The predictions from our models are evaluated on a data set from wind farms in western Denmark using a sliding window approach, for which estimation is performed using only the last available measurements. The case study shows that it is important to have a spatiotemporal model instead of a temporal one to achieve calibrated aggregated forecasts. Furthermore, spatiotemporal models have the advantage of being able to produce spatially out‐of‐sample forecasts. We use a Bayesian hierarchical framework to obtain fast and accurate forecasts of wind power generation not only at wind farms where recent data are available but also at a larger portfolio including wind farms without recent observations of power production. The results and the methodologies are relevant for wind power forecasts across the globe and for spatiotemporal modeling in general.
Benefits of spatiotemporal modeling for short‐term wind power forecasting at both individual and aggregated levels
The share of wind energy in total installed power capacity has grown rapidly in recent years. 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 spatiotemporal models for wind power generation and obtain full probabilistic forecasts from 15 min to 5 h ahead. Detailed analyses of forecast performances on individual wind farms and aggregated wind power are provided. The predictions from our models are evaluated on a data set from wind farms in western Denmark using a sliding window approach, for which estimation is performed using only the last available measurements. The case study shows that it is important to have a spatiotemporal model instead of a temporal one to achieve calibrated aggregated forecasts. Furthermore, spatiotemporal models have the advantage of being able to produce spatially out‐of‐sample forecasts. We use a Bayesian hierarchical framework to obtain fast and accurate forecasts of wind power generation not only at wind farms where recent data are available but also at a larger portfolio including wind farms without recent observations of power production. The results and the methodologies are relevant for wind power forecasts across the globe and for spatiotemporal modeling in general.
Benefits of spatiotemporal modeling for short‐term wind power forecasting at both individual and aggregated levels
Lenzi, Amanda (author) / Steinsland, Ingelin (author) / Pinson, Pierre (author)
Environmetrics ; 29
2018-05-01
1 pages
Article (Journal)
Electronic Resource
English
Traffic Speed Time Series Short Term Forecasting Using Aggregated Model
| British Library Conference Proceedings | 2014
Spatiotemporal traffic-flow dependency and short-term traffic forecasting
| Online Contents | 2008
Benefits of short-term photovoltaic power production forecasting to the power system
| Springer Verlag | 2021
Study on Medium-Term and Short-Term Wind Power Forecasting Methods
| British Library Conference Proceedings | 2013
Adaptabilities of three mainstream short-term wind power forecasting methods
| American Institute of Physics | 2015