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

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

Time series forecasting in the sensing system aims to predict future values based on historical records that sensors have collected. Previous works, however, usually focus on improving model structure or algorithm for better performance, while the perspective of learning proper numeric representations is overlooked. The inappropriate and coarse numeric representations are not expressive enough to capture the intrinsic characteristics of numbers, which will obviously degrade the prediction performance. In this paper, we propose our Num2vec, an algorithmic framework to learn numeric representations. Specifically, Num2vec lists three main logic characteristics of numbers, which are arithmetic, direction, and periodicity. By representing numbers into a transition space, Num2vec can translates numbers agilely to different IoT tasks through selecting the corresponding characteristics. According to such a design, Num2vec enjoys flexible numeric representations to fit different IoT time series tasks. Extensive experiments on four real-world datasets show that the approach achieves the best performance compared with state-of-the-art baselines.

Num2Vec: Pre-training Numeric Representations for Time Series Forecasting in Sensing System

Integration of wind power into the grid has been rapidly increasing at both the transmission as well as distribution levels. Wind power generation is variable, nonlinear, and intermittent in nature. The monthly average and maximum wind power generation vary over the year. To effectively integrate wind power into the grid, it is vital to provide forecasting for different months. Therefore, the machine learning technique has been applied to forecast the wind power generation for each month separately. Its accuracy, root mean square error (RMSE), mean absolute error (MAE), and standard deviation (SD) of forecasting error have been analyzed for every month and the whole year.

Wind Power Prediction in Different Months of the Year Using Machine Learning Techniques

The state-of-the-art development of spatial correlation based wind speed prediction is reviewed.And the concepts of conditional correlation and its corresponding confidence correlation are introduced to improve traditional spatial correlation.Based on big-data thinking, a framework of integrating data-driven with causality-driven wind speed prediction is proposed.In the framework, correlation is mined from historical data for wind speed prediction.Spatial correlation is employed to import data sources for wind speed prediction to overcome the shortage of historical data in part.Furthermore, spatial correlation with long time lag can be used to predict drastic and sudden change in downstream wind speed.Finally, suggestions for future research under the proposed framework can be made with confidence.

Review on Wind Speed Prediction Based on Spatial Correlation

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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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