# Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure

arXiv: https://arxiv.org/abs/2308.03472 · HTML: https://mlsanity.com/paper/2308.03472/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 58 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Forecasting: theory and practice](https://mlsanity.com/paper/2012.03854/) (2020)
- [Hierarchical forecast reconciliation with machine learning](https://mlsanity.com/paper/2006.02043/) (2020)
- [State-of-the-art predictive and prescriptive analytics for IEEE CIS 3rd Technical Challenge](https://mlsanity.com/paper/2112.03595/) (2021)
- [Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives](https://mlsanity.com/paper/2006.08570/) (2020)
- [How to effectively use machine learning models to predict the solutions for optimization problems: lessons from loss function](https://mlsanity.com/paper/2105.06618/) (2021)
- [Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues](https://mlsanity.com/paper/2303.17277/) (2023)
- [Model selection in reconciling hierarchical time series](https://mlsanity.com/paper/2010.10742/) (2020)

## Built on by (most cited first, 1 of 1)

- [Local vs. Global Models for Hierarchical Forecasting](https://mlsanity.com/paper/2411.06394/) (2024)

## Abstract (arXiv)

Renewable energy generation is of utmost importance for global decarbonization. Forecasting renewable energies, particularly wind energy, is challenging due to the inherent uncertainty in wind energy generation, which depends on weather conditions. Recent advances in hierarchical forecasting through reconciliation have demonstrated a significant increase in the quality of wind energy forecasts for short-term periods. We leverage the cross-sectional and temporal hierarchical structure of turbines in wind farms and build cross-temporal hierarchies to further investigate how integrated cross-sectional and temporal dimensions can add value to forecast accuracy in wind farms. We found that cross-temporal reconciliation was superior to individual cross-sectional reconciliation at multiple temporal aggregations. Additionally, machine learning based forecasts that were cross-temporally reconciled demonstrated high accuracy at coarser temporal granularities, which may encourage adoption for short-term wind forecasts. Empirically, we provide insights for decision-makers on the best methods for forecasting high-frequency wind data across different forecasting horizons and levels.