Forecasting wind power - an ensemble technique with gradual coopetitive weighting based on weather situation

Gensler, André; Sick, Bernhard

doi:10.1109/ijcnn.2016.7727855

Cited by 16 publications

(16 citation statements)

References 19 publications

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“…The cluster-based MSDA achieved an improvement of 20.63% compared to a traditional MSDA approach. [15,16] present the coopetitive soft-gating ensemble (CSGE) in the context of renewable power forecasts. It comprises a hierarchical two-stage ensemble prediction system and weights the ensemble member's predictions based on three aspects, namely global, local, and time-dependent performance.…”

Section: Wind Power Forecastingmentioning

confidence: 99%

Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid

Schreiber

2019

Preprint

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In recent years, transfer learning gained particular interest in the field of vision and natural language processing. In the research field of vision, e.g., deep neural networks and transfer learning techniques achieve almost perfect classification scores within minutes. Nonetheless, these techniques are not yet widely applied in other domains. Therefore, this article identifies critical challenges and shows potential solutions for power forecasts in the field of renewable energies. It proposes a framework utilizing transfer learning techniques in wind power forecasts with limited or no historical data. On the one hand, this allows evaluating the applicability of transfer learning in the field of renewable energy. On the other hand, by developing automatic procedures, we assure that the proposed methods provide a framework that applies to domains in organic computing as well.

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Section: Wind Power Forecastingmentioning

confidence: 99%

Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid

Schreiber

2019

Preprint

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“…The main contribution of this article is an extended coopetitive soft gating ensemble approach. It generalizes the original CSGE method proposed in [8], [9] for wind power forecasting to other ML tasks including regression, classification, and time series forecasting. The main contributions of this article are:…”

Section: Main Contributionmentioning

confidence: 99%

“…In [8], [9], the CSGE was presented in the context of renewable energy power forecasting. It comprises a hierarchical twostage ensemble prediction system and weights the ensemble member's predictions based on three aspects, namely global, local, and time-dependent performance.…”

Section: Related Workmentioning

confidence: 99%

“…a(x) weights the η and penalises when choosing η = 0 or η too high. Typically the parameter η lies in the range of 1.0 ≤ η ≤ 6.0 [8].…”

Section: G Regularisation Heuristicmentioning

confidence: 99%

“…In [8], [9], and [10] it is statistically shown that the CSGE can achieve state-of-the-art performance in the area of power forecasting. In this article, we aim to extend the original approach to general ML problems to show its potential for a wide range of problems including SISSY applications.…”

Section: Introductionmentioning

confidence: 99%

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Coopetitive Soft Gating Ensemble

Deist¹,

Bieshaar²,

Schreiber³

et al. 2018

Preprint

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In this article, we propose the Coopetititve Soft Gating Ensemble or CSGE for general machine learning tasks and interwoven systems. The goal of machine learning is to create models that generalize well for unknown datasets. Often, however, the problems are too complex to be solved with a single model, so several models are combined. Similar, Autonomic Computing requires the integration of different systems. Here, especially, the local, temporal online evaluation and the resulting (re-)weighting scheme of the CSGE makes the approach highly applicable for self-improving system integrations. To achieve the best potential performance the CSGE can be optimized according to arbitrary loss functions making it accessible for a broader range of problems. We introduce a novel training procedure including a hyper-parameter initialisation at its heart. We show that the CSGE approach reaches state-of-the-art performance for both classification and regression tasks. Further on, the CSGE provides a human-readable quantification on the influence of all base estimators employing the three weighting aspects. Moreover, we provide a scikit-learn compatible implementation.

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A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

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With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.

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Forecasting wind power - an ensemble technique with gradual coopetitive weighting based on weather situation

Cited by 16 publications

References 19 publications

Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid

Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid

Coopetitive Soft Gating Ensemble

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

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