Wind speed prediction with spatio–temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–temporal correlation. This paper proposes a model for wind speed prediction with spatio–temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–temporal correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–temporal correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.

Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach

The invention discloses an ARIMA (Autoregressive integrated moving average) model load prediction based parallelization computing method. The method comprises the following steps: partitioning original electrical load data to form time series data according to the electrical load characteristic and strong relevance of data at the same moments every day; newly constructing a plurality of threads, synchronizing all threads, setting a counter shared globally, executing run () function to parallelize processed and partitioned data by use of the newly constructed thread, judging whether the counter in run () is locked, if so, queuing up; if not, locking the counter in the run (), and counting by adding 1; predicting by calling ARIMA according to data in the time series data, corresponding to the numerical value of the counter; and acquiring the predicted data, unlocking, repeating the steps until data in the time sequence data is completely processed. The method can remarkably improve the computing efficiency.

ARIMA (Autoregressive integrated moving average) model load prediction based parallelization computing method

This paper analyses importance of correlating wind speed (WS) and wind direction (WD) for a more confident evaluation of uncertainty in wind turbine (WT) power output ($\mathrm {P}_{\mathrm {o}\mathrm {u}\mathrm {t}}$). Using the available measurements of actual WTs, the paper first presents a new model for the analysis of the $\mathrm {P}_{\mathrm {o}\mathrm {u}\mathrm {t}}$-WS-WD correlations, based on Gaussian mixture Copula model (GMCM) and vine Copula (i.e., vine-GMCM framework). Afterwards, the paper compares results of a two-dimensional $\mathrm {P}_{\mathrm {o}\mathrm {u}\mathrm {t}}$-WS-WD model, previously proposed by some of the authors, with the cross-correlated three-dimensional $\mathrm {P}_{\mathrm {o}\mathrm {u}\mathrm {t}}$-WS-WD model, demonstrating that the ranges of variations of $\mathrm {P}_{\mathrm {o}\mathrm {u}\mathrm {t}}$ can be better modelled by considering not only wind speed, but also wind direction.

On the Importance of Correlating Wind Speed and Wind Direction for Evaluating Uncertainty in Wind Turbine Power Output

The invention discloses a wind power plant full-field power predication method. The method includes the following steps: wind power plants judge whether weather forecast information can be obtained, and if a judgment result shows that the weather forecast information can be obtained, the wind power plants conduct power prediction or combine the weather forecast information with the wind speed predicated by the wind power plants to conduct power predication on the basis of the weather forecast information; and if the judgment result shows that the weather forecast information cannot be obtained, the wind power plants train a wind speed predication model through historical data, a predicated wind speed is obtained through calculation by the wind speed predication model, power predication is performed on the basis of the predicated wind speed, a predication result is sent to a power grid scheduling center, and the power grid scheduling center makes subsequent grid connection operation plans on the basis of the predicated power and arranges maintenance operations to be conducted in a time period/region with low electric quantity capacity. According to the method, operations can be carried out without weather forecast data, so the adaptability of a system is greatly improved, and the prediction precision can be further improved in the presence of meteorological conditions.

Wind power plant full-field power predication method

The embodiment of the invention discloses a high-speed train running speed control method and device, a storage medium and a high-speed train. The method comprises acquiring real-time air speed data at an air speed detecting position as model input data, and inputting the model input data into an air speed prediction model which is a statistical model obtained by training of an initial statisticalmodel according to the historical air speed data at the air speed detecting position, wherein the initial statistical model comprises an AR model, an MA model and an ARMA model, acquiring an air speed prediction result according to the air speed prediction model, determining a predicted air speed when the high-speed train runs to the air speed detecting position according to the air speed prediction result, and providing running speed control information to the high-speed train according to the predicted air speed. The technical scheme provided by the embodiment realizes control of a train running speed according to an air speed and improves the safety of the high-speed train in running.

High-speed train running speed control method and device, storage medium and high-speed train

The invention provides an ultra-short period wind power prediction method based on a wind speed factor control model. The ultra-short period wind power prediction method includes performing an ultra-short period prediction according to an ARIMA (autoregressive integrated moving average) model; performing the ultra-short period power prediction according to a BP (back propagation) neutral network model; completing the ultra-short period wind power prediction based on the wind speed factor control model. In this way, at the premise that the ARIMA model and the BP neutral network model are evaluated, the ARIMA model and the BP neutral network model can be switched according to the running environment, so that accuracy of the ultra-short wind power prediction is improved.

Ultra-short period wind power prediction method based on wind speed factor control model

This paper investigates the impact of wind direction on the prediction of wind power. A novel short-term wind power forecasting model is developed based on the data of wind speed, wind direction, wind farm output and numerical weather prediction. Statistical methods are adopted for the data analysis. To show the effectiveness, the approach is applied on a wind farm in north China. The average RMSE is reduced by 41.3% through the application of the proposed approach.

The influence of wind direction on short-term wind power prediction: A case study in north China

This paper presents three methods for the mapping model for provincial wind power prediction. After correlation analysis of the historical data, several wind farms' output power are found to be principal related to the global provincial wind power. For the first method, curve fitting and weighted average values are used to establish the mapping model. The second method is based on multiple linear regression. The third method depends on radial base function networks to search the unpredictable mapping. Finally, these methods are tested by forecasting horizon of 24h ahead and compared with their performance, which shows the validity of them.

Research on the mapping model for provincial wind power prediction

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Papers

Ultra-short period wind power prediction method based on wind speed factor control model

The influence of wind direction on short-term wind power prediction: A case study in north China

Research on the mapping model for provincial wind power prediction