# A Multi-Scheme Ensemble Using Coopetitive Soft-Gating With Application to Power Forecasting for Renewable Energy Generation

arXiv: https://arxiv.org/abs/1803.06344 · HTML: https://mlsanity.com/paper/1803.06344/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts](https://mlsanity.com/paper/2204.13293/) (2022)
- [VAEneu: A New Avenue for VAE Application on Probabilistic Forecasting](https://mlsanity.com/paper/2405.04252/) (2024)
- [Transfer Learning as an Essential Tool for Digital Twins in Renewable Energy Systems](https://mlsanity.com/paper/2203.05026/) (2022)

## Abstract (arXiv)

In this article, we propose a novel ensemble technique with a multi-scheme weighting based on a technique called coopetitive soft gating. This technique combines both, ensemble member competition and cooperation, in order to maximize the overall forecasting accuracy of the ensemble. The proposed algorithm combines the ideas of multiple ensemble paradigms (power forecasting model ensemble, weather forecasting model ensemble, and lagged ensemble) in a hierarchical structure. The technique is designed to be used in a flexible manner on single and multiple weather forecasting models, and for a variety of lead times. We compare the technique to other power forecasting models and ensemble techniques with a flexible number of weather forecasting models, which can have the same, or varying forecasting horizons. It is shown that the model is able to outperform those models on a number of publicly available data sets. The article closes with a discussion of properties of the proposed model which are relevant in its application.