Wind Power Forecasting with Machine Learning: Single and combined methods
In one sentence. Machine learning pipeline to improve NWP-based wind power forecasts in Portugal, targeting under 10% error; results are modest and the method is not novel.
Abstract
In Portugal, wind power represents one of the largest renewable sources of energy in the national energy mix. The investment in wind power started several decades ago and is still on the roadmap of political and industrial players. One example is that by 2030 it is estimated that wind power is going to represent up to 35% of renewable energy production in Portugal. With the growth of the installed wind capacity, the development of methods to forecast the amount of energy generated becomes increasingly necessary. Historically, Numerical Weather Prediction (NWP) models were used. However, forecasting accuracy depends on many variables such as on-site conditions, surrounding terrain relief, local meteorology, etc. Thus, it becomes a challenge to obtain improved results using such methods. This article aims to report the development of a machine learning pipeline with the objective of improving the forecasting capability of the NWP’s to obtain an error lower than 10%.
Labels
- Topic
- Applications & impacts, Post-processing & calibration
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.24084/repqj20.297 OpenAlex:W4293092153
- Added
- 2026-10-22
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