Data mining for wind power forecasting

International audienceShort-term forecasting of wind energy production up to 2-3 days ahead is recognized as a major contribution for reliable large-scale wind power integration. Increasing the value of wind generation through the improvement of prediction systems performance is recognised as one of the priorities in wind energy research needs for the coming years. This paper aims to evaluate Data Mining type of models for wind power forecasting. Models that are examined include neural networks, support vector machines, the recently proposed regression trees approach, and others. Evaluation results are presented for several real wind farms

Uncertainty estimation of wind power forecasts: Comparison of Probabilistic Modelling Approaches

International audienceShort-term wind power forecasting tools providing “single-valued” (spot) predictions are nowadays widely used. However, end-users may require to have additional information on the uncertainty associated to the future wind power production for performing more efficiently functions such as reserves estimation, unit commitment, trading in electricity markets, a.o. Several models for on-line uncertainty estimation have been proposed in the literature and new products from numerical weather prediction systems (ensemble predictions) have recently become available, which has increased the modelling possibilities. In order to provide efficient on-line uncertainty estimation, choices have to be made on which model and modelling architecture should be preferred. Towards this goal we proposes to classify different approaches and modelling architectures for probabilistic wind power forecasting. Then, a comparison is carried out on representatives models using real data from several wind farms

Probabilistic short-term wind power forecasting based on kernel density estimators

International audienceShort-term wind power forecasting tools have been developed for some time. The majority of such tools usually provide single-valued (spot) predictions. Such predictions are however often not adequate when the aim is decision-making under uncertainty. In that case there is a clear requirement by end-users to have additional information on the uncertainty of the predictions for performing efficiently functions such as reserves estimation, unit commitment, trading in electricity markets, a.o. In this paper, we propose a method for producing the complete predictive probability density function (PDF) for each time step of the prediction horizon based on the kernel density estimation technique. The performance of the proposed approach is demonstrated using real data from several wind farms. Comparisons to state-of-the-art methods from both outside and inside the wind power forecasting community are presented illustrating the performances of the proposed method

Advanced strategies for wind power trading in short-term electricity markets

International audienceIndependent power producers have the possibility to participate in short-term electricity markets to trade wind power energy in several countries in Europe. Under such market context, penalties may apply for differences between the contracted energy and the produced energy. The limited predictability of the wind resource may thus result to a reduction of the competitiveness of wind power generation. In this paper, we propose a risk-based decision approach for optimizing the benefits of an energy producer who submits energy bids in a day-ahead electricity market. Loss functions are used to model the penalties resulting from imbalances. For achieving this, we use wind power probabilistic forecasts. The benefits from the approach are demonstrated using real-word data for a whole year

Probabilistic short-term wind power forecasting for the optimal management of wind generation.

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Management of energy storage coordinated with wind power under electricity market conditions

International audienceUnder electricity market operation, the competitiveness of wind power generation may be reduced because of the stochastic nature of the wind resource, which often results in increased regulation costs. The negative impact associated to the stochastic nature of the wind resource may be reduced by coupling the wind farm with energy storage facilities, thus constituting a virtual power plant. In this paper, a novel method is proposed for scheduling and operating such a plant in an electricity market environment. The proposed method is able to take advantage of existing market opportunities for increasing operational profits while smoothing out energy imbalances caused by the errors associated to wind power forecasts. The results obtained for a test case based on real-world data for a whole year of operation are compared and discussed

A spot-risk-based approach for addressing problems of decision-making under uncertainty

International audienceConsiderable research has been devoted on the development of decision-making models suitable for tackling decision problems integrating some amount of uncertainty. However, such approaches are either problem-specific, either too general to suit certain problems. In this paper we propose an approach for performing decision-making under uncertainty suitable for problems in which decisions must be made sequentially (i.e.: in time). We apply the proposed model to the case of a virtual power plant (comprising a wind farm and an energy storage device) operating under market conditions with the objective of reducing the imbalances generated by the plant while maximizing the profit of the plant operator. Using real-world data, we have simulated and evaluated several operation scenarios for an entire year of operation. Wind power forecasts produced by a state-of-art wind power forecasting model (based on KDE estimators) were used

I. Short-term forecasting of offshore wind farm production. Developments of the Anemos project.

Disponible sur internet : http://www.ewec2006proceedings.info/allfiles2/968_Ewec2006fullpaper.pdfInternational audienc