Besides, another impetus for the rapid development of **wind** **power** is the increasingly serious problem of global climate change which is known as the most seriously worldwide recognized environmental threat. In the 1997 Kyoto protocol, developed countries reached an agreement to decrease the totality of the emissions of six kinds of greenhouse gas at least 5% from 1990 levels in 2008-2010 (8% for Europe, 7% for United States and 6% for Canada). In addition, the development of nuclear energy is constrained by concerning about the radioactive waste. Especially the leakage of nuclear materials of Japan Fukushima nuclear **power** plant in March 2011 made countries around the world to reconsider their nuclear policies. Nuclear **power** development in China has therefore been put off, while Germany will shut down all nuclear **power** plants by 2022. Thus, the declination of the status of nuclear **power** in clean energy makes the development of other clean energy like **wind** **power** more urgent.

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The concept of deep learning originates from the study of artificial neural networks. Multilayer perceptron with multiple hidden layers is a type of deep learning structure. Deep learning can combine low-level features to form more abstract high-level ones, which can represent an attribute category or feature to discover the distributed representations of data features. Deep learning overcomes the problems of over-fitting and slow training **speed** in traditional neural networks. The long **short**-**term** memory network (LSTM) algorithm represents the development of the recurrent neural network (RNN) circulation neural network, which is a special form of RNN. However, its universality is much better than that of traditional RNN. Many scholars use the LSTM algorithm for **forecasting** research [8] in many areas. LSTM has been used for **short**- **term** traffic **forecasting** [9] and for **forecasting** hourly day-ahead solar irradiance **based** on weather **forecasting** data [10]. In the latter study, LSTM is used to improve the prediction accuracy of back propagation neural networks (BPNN); however, the predicted time interval is hourly. Wang et al. [11] put forward a deep belief network (DBN) **based** on **wind** **speed** prediction models; their case studies verify that the proposed model is accurate and stable.

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He et al. presents a two stages approach [21]:(1) offline spatial-temporal analysis carried out on historical data with multiple finite-state Markov chains; (2) online **forecasting** by feeding a Markov chain with real-time measurements of the **wind** turbines. Similar to previous works, different sparse structures of the spatial-temporal relations are not fully explored. The same authors in [22] propose a different approach **based** on VAR model fitting with sparsity-constrained maximum likelihood. The main limitation of this approach is that the sparse coefficients are not automatically defined, instead, expert knowledge and partial correlation analysis are employed.

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This paper combines the mathematical statistics and **BP** neural network on **wind** **power** prediction, PSO algorithm is used to improve prediction precision. **Based** on which, **Wind**/Storage system is used to amend **wind** farm **power** forecast. Simulation results show that the proposed pretreatment (mathematical statistics method) can improve the neural network training **speed** and precision. In addition, the PSO algorithm can also improve the prediction precision of the **BP** neural network effectively. Compared with the current **wind** farm **forecasting** strategy, RMSE of PSO-**BP** can be reduced by 6.34%, and the MAE reduced by 0.4%. Moreover, the schedule forecast accuracy can be improved effectively by physical **Wind**/Storage dynamical correction, and experiments show that RMSE of **wind** **power** forecast has been reduced to 9.3535e+003W, the energy storage system can minify **wind** **power** prediction error effectively. However, limited by the technical conditions, the battery capacity is still an important bottleneck in application all along. The battery capacity can be insufficient for big share on the access of large **wind** farms, but it can be used as a **power** fine-tuning in large **wind** **power**.

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3) Specifically, through the comparisons with some recent methods, such as deep-learning-based DNN [12], the ef- fectiveness of our proposed model is validated on the short-term and [r]

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To this end, multivariate linear regression approach is introduced for efficient high-dimensional WPF. To avoid over- fitting problems and make models more interpretable, sparse modelling techniques are usually used to force regression coefficients of some less important variables to be zeros. Typically, sparse vector autoregression (VAR) models are frequently studied in spatio-temporal WPF. Dowell and Pinson [17] applied a two- stage sparse VAR for very-**short**-**term** probabilistic WPF by using the partial spectral coherence and some basic statistics to determine zero coefficients. Cavalcante et al. [18] described a **forecasting** methodology that explores a set of different sparse structures for VAR models **based** on the Least Absolute Shrinkage and Selection Operator (LASSO) framework. Zhao et al. [19] presented a correlation-constrained and sparsity-controlled VAR model by transforming the VAR optimisation into a mixed-integer non-linear programming, which allows both freely controlling sparsity and incorporating expert knowledge on spatial correlation into the **forecasting** model.

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In this context, information from WPP time series distributed in space can be used to improve the forecast skill of each WPP. The first results were presented by Gneiting et al. for two hours-ahead **wind** **speed** **forecasting** [11]. The authors showed that a Regime-Switching Space-Time Diurnal model that takes advantage of temporal and spatial correlation from geographically dispersed meteorological stations as off-site predictors can have a root mean square error (RMSE) 28.6% lower than the persistence forecasts. Expert knowledge and empirical results were used to select the predictors. In [12], two additional statistical models are proposed, Trigonometric Direction Diurnal model and Bivariate Skew-T model. These results were generalized by Tastu et al. by studying the spatio-temporal propagation of **wind** **power** forecast errors [13] . The authors showed evidences of cross-correlation functions with significant dependency in lags of a few hours.

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For the sake of comparison, a back propagation feed-forward neural network similar to the work of [3] is employed for constructing the artificial neural network and **forecasting** the 1-hour ahead **wind** **speed** data. Among supervised structures, ANN is the most commonly used one and has been successfully adopted for both **short**- and long-**term** **forecasting** of time series where normally a defined error function, which is typically mean square error, is minimized using a gradient descent method [12]. A weight is being assigned to connect every two nodes. During the training phase, these weights have to be set in order of minimizing a predefined error function which is typically mean squared error (MSE). By setting the weights, the network is applicable to any unknown sample. ANN could be trained for any sort of problem including clustering, classification and regression. In this work, the trained ANN is used for prediction which lies in the regression category. Levenberg–Marquardt algorithm (LMA) is used for weight selection of the ANN structure. LMS has been used in many curve-fitting applications for finding the minimum though it is not a guarantee that it could find the global minimum. Other algorithms with a better success rate in finding the global minimum could be also applied, including the evolutionary algorithms like artificial bee colony [13] etc. However, as the primary concern of the study is the ANFIS performance in **wind**-**speed** **forecasting** and its comparison with a typical ANN, the enhancement in the neural network performance is not to be investigated. C. Theory of ANFIS

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A number of **wind** turbine parameters are collected as the training samples via the sensor unit. However, these samples may contain unreasonable data. Besides, using too many parameters as the training features would increase the computing complexity and obtain undesired results for the reason that some variables are irrelevant or redundant in this model. Selecting features which are most related to the **wind** **power** is able to improve the accuracy. Finally, data normalization has an effect on the convergence rate and accuracy of the training algorithm. Thus, in order to obtain accurate **forecasting** results, data preprocessing is necessary. 3.1.1 Data cleaning

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ahead, while KF and NN cause 1.6120 m·s −1 and 1.6047 m·s −1 , respectively. Figure 4 shows the calculated MAE of 1 - 30 ahead **wind** **speed** forecast. In **wind** **speed** fore- casting, as forecast time lengthens, forecast error in- creases. This phenomenon appears prominently in the result shown in Figure 4. There are no differences in calculated results of MAE with AR model, KF and NN. But the forecast results of these models vary on a case-by-case basis with the characteristic (stochastic ap- pearance) of **wind** **speed**. In the case of AR model, Fig- ure 3(a) shows that the forecast result of 10 sec ahead **wind** **speed** is influenced by gradient between forecast time and before 10 sec **wind** **speed**. In the case of NN, Figure 3(b) shows that the result of 10 sec ahead **wind**

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space-time diurnal (RSTD) model to forecast 2-hour-ahead **wind** **speed** at Vansycle, Oregon. Their model outperformed persistence forecasts and autoregressive forecasts by 29% and 13%, respectively, in terms of the root mean squared error (RMSE) in July 2003, for instance. However, the RSTD model relies on local geographic features. To remove these constraints, Hering and Genton (2010) generalized the RSTD model by treating **wind** direction as a circular variable and including it in their model. They coined it a trigonometric direction diurnal (TDD) model. The TDD model obtained similar or better **forecasting** results than did the RSTD model without requiring prior geographic information. Tastu et al. (2011) analyzed and modeled **short**-**term** **wind** **power** forecast errors using spatio-temporal methods, such as regime-switching models **based** on **wind** direction and conditional parametric models with regime- switching, substantially reducing variance in the forecast errors. Pinson and Madsen (2012) applied adaptive Markov-switching autoregressive models to offshore **wind** **power** **forecasting** problems in which the regime sequence is not directly observable but follows a first-order Markov chain. Here, a new modification of the RSTD model is proposed to allay its limitations.

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So far, several studies concerning the participation of **wind** energy in electricity markets have been carried out, considering different market mechanisms and various prediction method- ologies. Some of them focus on the use of point predictions and relate the accuracy of the forecasts to the resulting regu- lation costs [4], [5]. In contrast, methods described in [6] and [7] integrate information on prediction uncertainty for taking advantage of the asymmetry of imbalance prices, while [8] and [9] describe participation strategies **based** on optimal quantile forecasts of **wind** generation. Finally, [10] proposes a complete methodology that accounts for the uncertainty in both **wind** **power** predictions and imbalance prices by using scenarios of **power** production and imbalances prices, consequently used in a stochastic optimization problem. Our aim in the present paper is to compare the market value of different **wind** **power** **forecasting** methods and their associated bidding strategies. Two rival approaches are considered, i.e., point **forecasting** and probabilistic **forecasting** methods. It is shown how the latter ones outperform the former ones when used in conjunction with appropriate bidding strategies. Such optimal bidding strategies are described. They use the utility theory in order to build a model of the sensitivity of the market participant to regulation costs. Both probabilistic forecasts and this model are consequently integrated in a suitable decision-making process in a stochastic optimization framework.

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In this paper, a compromise approach of **short**-**term** **wind** **power** forecasts **based** on three clustering methods, K-means, self-organizing map (SOM), and spectral clustering (SC), are employed to cluster **wind** turbines into groups by capturing the similarity of **wind** **speed** and **wind** **power**, decreasing the forecast error by smoothing effects of multi-clusters. Sihouette coefficient and Hopkins statistics indices are used to determine the optimal cluster number. The **forecasting** **wind** information like **speed** and direction are usually employed to forecast the out- put **power**. However, there is no available matured numerical weather prediction (NWP) data. So this work employs the real **wind** **speed** in replace of the NWP data and historical output **power** of each **wind** turbine in a **wind** farm for **forecasting**. Comparing three models, the better model can be used to provide scientific guidance for the operation and dispatch of **wind** farms.

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While CDSO, GA and PSO algorithms all provide a zeroth-order optimization ap- proach, meaning that they do not need gradient information, it does not necessarily mean that these methods would always be superior to gradient **based** algorithms. In this respect, it would be interesting to integrate the gradient boosting machine (GBM) to the boosted projection pursuit regression algorithm, to explore the advantage of GBM and investigate the potential to improve the performance of gradient-free algo- rithms.

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The underlying physical processes governing the spatio-temporal structure of **wind** speeds are complex, but we have shown that useful infor- mation may be extracted from gridded weather data using the methods described above to discriminate between distinct regimes. We therefore advocate efforts to include atmospheric conditions (or regimes more generally) when producing very **short**-**term** **wind** **speed** and **power** forecasts, especially given that appropriate information is widely available. This information may be present in the historic data of target variables, as in Kazor and Hering, 25 for example, or be found in supplementary data, as presented here. Future work should also consider **forecasting** the future mode

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The underlying physical processes governing the spatio-temporal structure of **wind** speeds are complex, but we have shown that useful infor- mation may be extracted from gridded weather data using the methods described above to discriminate between distinct regimes. We therefore advocate efforts to include atmospheric conditions (or regimes more generally) when producing very **short**-**term** **wind** **speed** and **power** forecasts, especially given that appropriate information is widely available. This information may be present in the historic data of target variables, as in Kazor and Hering, 25 for example, or be found in supplementary data, as presented here. Future work should also consider **forecasting** the future mode

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showed that although some Neural Networks outperform others, this improved accuracy comes at the expense of longer training time. Similarly, the use of training data spanning only one week may not allow an analysis against long-**term** (seasonal) effects. Reikard also used Neural Networks for **short**-**term** **wind** **speed** **forecasting** with both **wind** **speed** and temperature used for training (Reikard 2008). The use of additional meteorological data (i. e. temperature) in the training was found to reduce the forecast error for **wind** **speed** but the methodology applied also showed that Neural Network prediction accuracy decreases as the temporal prediction range grows (for longer periods ahead). The current paper will show that seasonal effects should be factored in with long-**term** predictions but the prediction accuracy is strongly affected by the length of (historical) data used for training the Neural Network (varied between 5 hours and 168 hours). Alternatively, long-**term** **wind** **power** **forecasting** has been conducted by Cali at al. (Cali et al. 2008) using a multi-model approach with **wind** **speed**, **wind** direction, ambient pressure, temperature and humidity as training data.

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The non-linear **wind** turbine **power** curve motivates to analyse whether the strength of wake-induced **power** fluctuations is dependent on the average **wind** **speed**. For this purpose, **power** fluctuation differences are conditioned to mean **wind** **speed** of the respective free-flow turbines and to **wind** direction. The mean **wind** **speed** is computed as a 2 h average. Figure 7 shows that highest fluctuations occur in the **wind** **speed** interval of 10–15 m/s, where **wind** **speed** is fluctuating around the rated **wind** **speed** of 12–13 m/s. Conclusively, sometimes the nominal **power** is reached and sometimes not, while the free-flow turbine is steadily operating at the nominal **power**. In addition, the wake-induced and free flow fluctuations are smaller when **wind** speeds are consistently above the rated **wind** **speed** i.e., > 15 m/s. For lower **wind** **speed** intervals, the fluctuations are relatively insignificant at all **wind** directions.

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In this paper, a compromise approach of **short**-**term** **wind** **power** forecasts **based** on three clustering methods, K-means, self-organizing map (SOM), and spectral clustering (SC), are employed to cluster **wind** turbines into groups by capturing the similarity of **wind** **speed** and **wind** **power**, decreasing the forecast error by smoothing effects of multi-clusters. Sihouette coefficient and Hopkins statistics indices are used to determine the optimal cluster number. The **forecasting** **wind** information like **speed** and direction are usually employed to forecast the out- put **power**. However, there is no available matured numerical weather prediction (NWP) data. So this work employs the real **wind** **speed** in replace of the NWP data and historical output **power** of each **wind** turbine in a **wind** farm for **forecasting**. Comparing three models, the better model can be used to provide scientific guidance for the operation and dispatch of **wind** farms.

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This paper investigates **short**-**term** **forecasting** for **wind** **power** system. Since **power** scheduling is the major problem in integrating **wind** **power** into the grid **power** system. However it follows a sub -class referred to as very **short**-**term** **forecasting**. Very-**short**-**term** **forecasting** is predominantly focused on predicting the value of the next period for a week to year depending on applicable data set. In this instance, that period of a year data is used **based** on operating cycle of a suzlon machine.

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