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Benefits of spatiotemporal modeling for short‐term wind power forecasting at both individual and aggregated levels

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  • Amanda Lenzi
  • Ingelin Steinsland
  • Pierre Pinson

Abstract

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.

Suggested Citation

  • Amanda Lenzi & Ingelin Steinsland & Pierre Pinson, 2018. "Benefits of spatiotemporal modeling for short‐term wind power forecasting at both individual and aggregated levels," Environmetrics, John Wiley & Sons, Ltd., vol. 29(3), May.
  • Handle: RePEc:wly:envmet:v:29:y:2018:i:3:n:e2493
    DOI: 10.1002/env.2493
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    Cited by:

    1. Fatemeh Hassanzadeh, 2021. "A smoothing spline model for multimodal and skewed circular responses: Applications in meteorology and oceanography," Environmetrics, John Wiley & Sons, Ltd., vol. 32(2), March.
    2. Lucheroni, Carlo & Boland, John & Ragno, Costantino, 2019. "Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models," Applied Energy, Elsevier, vol. 239(C), pages 1226-1241.
    3. I Gede Nyoman Mindra Jaya & Henk Folmer, 2024. "High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia," Mathematics, MDPI, vol. 12(18), pages 1-29, September.
    4. Hugo Algarvio & António Couto & Fernando Lopes & Ana Estanqueiro, 2019. "Changing the Day-Ahead Gate Closure to Wind Power Integration: A Simulation-Based Study," Energies, MDPI, vol. 12(14), pages 1-20, July.
    5. Sun, Mucun & Feng, Cong & Zhang, Jie, 2019. "Conditional aggregated probabilistic wind power forecasting based on spatio-temporal correlation," Applied Energy, Elsevier, vol. 256(C).
    6. Mashlakov, Aleksei & Kuronen, Toni & Lensu, Lasse & Kaarna, Arto & Honkapuro, Samuli, 2021. "Assessing the performance of deep learning models for multivariate probabilistic energy forecasting," Applied Energy, Elsevier, vol. 285(C).
    7. Sundararajan, Raanju R., 2021. "Principal component analysis using frequency components of multivariate time series," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).

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