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

TL;DR

This paper introduces a novel ensemble method using coopetitive soft gating that combines multiple forecasting models hierarchically to improve power prediction accuracy for renewable energy, outperforming existing models.

Contribution

The paper presents a new multi-scheme ensemble technique that integrates competition and cooperation among models, enhancing forecasting accuracy for renewable energy generation.

Findings

Outperforms existing power forecasting models on public datasets

Flexible in handling various weather models and lead times

Effective in combining multiple ensemble paradigms hierarchically

Abstract

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.