Data mining and wind power prediction: A literature review

Çolak, İlhami; Sağıroğlu, Şeref; Yeşilbudak, Mehmet

doi:10.1016/j.renene.2012.02.015

Cited by 195 publications

(81 citation statements)

References 42 publications

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“…Relative to their output, imbalances are larger for these than for other generators. Forecasting methodologies are relatively new and the potential for improvement seems to be vast, both for wind power (dena 2010, Foley et al 2012, Colak et al 2012, Freedman et al 2013, Siefert et al 2013, Hong et al 2014) and solar power (Chen et al 2011, Fernandez-Jimenez et al 2012). …”

Section: The Balancing Price: An Incentive For Better Forecastingmentioning

confidence: 99%

Balancing power and variable renewables: Three links

Hirth

Ziegenhagen

2015

Renewable and Sustainable Energy Reviews

272

148

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-Balancing power is used to quickly restore the supply-demand balance in power systems. The need for this tends to be increased by the use of variable renewable energy sources (VRE) such as wind and solar power. This paper reviews three channels through which VRE and balancing systems interact: the impact of VRE forecast errors on balancing reserve requirements; the supply of balancing services by VRE generators; and the incentives to improve forecasting provided by imbalance charges. The paper reviews the literature, provides stylized facts from German market data, and suggests policy options. Surprisingly, while German wind and solar capacity has tripled since 2008, balancing reserves have been reduced by 15%, and costs by 50%.

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Section: The Balancing Price: An Incentive For Better Forecastingmentioning

confidence: 99%

Balancing power and variable renewables: Three links

Hirth

Ziegenhagen

2015

Renewable and Sustainable Energy Reviews

272

148

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“…Note that, the predicted outputP i,w is a fixed value, which can be obtained by multiple wind power forecasting technologies [44], i.e., artificial neural network method, support vector machine regression method, neuro-fuzzy network method, etc. Thus, the value of β is determined by P W i,w , and β obtains its maximum value when wind power P W i,w reaches the lower or upper bounds of confidence interval.…”

Section: Analysis Of Wind Power Fluctuationsmentioning

confidence: 99%

An Extreme Scenario Method for Robust Transmission Expansion Planning with Wind Power Uncertainty

et al. 2018

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Abstract:The rapid incorporation of wind power resources in electrical power networks has significantly increased the volatility of transmission systems due to the inherent uncertainty associated with wind power. This paper addresses this issue by proposing a transmission network expansion planning (TEP) model that integrates wind power resources, and that seeks to minimize the sum of investment costs and operation costs while accounting for the costs associated with the pollution emissions of generator infrastructure. Auxiliary relaxation variables are introduced to transform the established model into a mixed integer linear programming problem. Furthermore, the novel concept of extreme wind power scenarios is defined, theoretically justified, and then employed to establish a two-stage robust TEP method. The decision-making variables of prospective transmission lines are determined in the first stage, so as to ensure that the operating variables in the second stage can adapt to wind power fluctuations. A Benders' decomposition algorithm is developed to solve the proposed two-stage model. Finally, extensive numerical studies are conducted with Garver's 6-bus system, a modified IEEE RTS79 system and IEEE 118-bus system, and the computational results demonstrate the effectiveness and practicability of the proposed method.

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“…The data-based models with wind speed, wind generator speed, voltage and current in all phases as inputs could achieve an accurate prediction of the wind power output [20]. For medium-term and long-term wind power prediction, ANN models, adaptive fuzzy logistic and multilayer perceptrons are the most popular kinds of methods [21][22][23]. Moreover, as the deep learning algorithms bloom, the CNN, long short term memory (LSTM), Deep Brief Net (DBN) and recurrent neural network (RNN) modelling have become popular in some renewable energy predictions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs

2018

Energies

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Successful development of a marine wave energy converter (WEC) relies strongly on the development of the power generation device, which needs to be efficient and cost-effective. An innovative multi-input approach based on the Convolutional Neural Network (CNN) is investigated to predict the power generation of a WEC system using a double-buoy oscillating body device (OBD). The results from the experimental data show that the proposed multi-input CNN performs much better at predicting results compared with the conventional artificial network and regression models. Through the power generation analysis of this double-buoy OBD, it shows that the power output has a positive correlation with the wave height when it is higher than 0.2 m, which becomes even stronger if the wave height is higher than 0.6 m. Furthermore, the proposed approach associated with the CNN algorithm in this study can potentially detect the changes that could be due to presence of anomalies and therefore be used for condition monitoring and fault diagnosis of marine energy converters. The results are also able to facilitate controlling of the electricity balance among energy conversion, wave power produced and storage.

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Data mining and wind power prediction: A literature review

Cited by 195 publications

References 42 publications

Balancing power and variable renewables: Three links

Balancing power and variable renewables: Three links

An Extreme Scenario Method for Robust Transmission Expansion Planning with Wind Power Uncertainty

Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs

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