A review of wind speed and wind power forecasting with deep neural networks

Photovoltaic power forecast based on satellite images considering effects of solar position

Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection

Very short-term forecasting of wind power generation using hybrid deep learning model

Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning

A deep reinforcement learning method for managing wind farm uncertainties through energy storage system control and external reserve purchasing

Different from power prediction for a single wind farm, the regional wind power prediction is to predict the total power of multiple wind farms located in the specific region. The regional wind power prediction involves more data that implicate abundant information on spatiotemporal correlations and nonlinearity. So that addressing the massive data and extracting the representative features became the crucial issues to construct an effective regional wind power prediction model. This article proposes a spatiotemporal quantile regression (QR) algorithm to perform the short-term nonparametric probabilistic prediction of regional wind power, incorporating the advantages of the hybrid neural network (HNN) and QR. In the approach, the high-dimensional input data are reorganized into a feature graph that is ready for feature extraction by the HNN. Therefore, the advantages of HNN can be utilized to extract the representative features and construct nonlinear regression models. Meanwhile, by following the QR rules, the model obtains quantiles and perform probabilistic prediction. By properly addressing the explanatory variable selection issue, the approach provides a specific solution for regional wind power probabilistic prediction with the massive input data. The test results in a region with ten wind farms demonstrate the effectiveness of the proposed approach.

Probabilistic Prediction of Regional Wind Power Based on Spatiotemporal Quantile Regression

Different from power prediction for a single wind farm, the regional wind power prediction is to predict the total power of multiple wind farms located in the same region. Normally, abundant information on spatiotemporal correlations and nonlinearity is implicated in regional wind farms, and thus selecting the most representative explanatory variables becomes one of the most crucial issues to construct an effective regional wind power prediction model. This paper proposes a spatiotemporal quantile regression (SQR) algorithm to perform short-term nonparametric probabilistic prediction of regional wind power, incorporating the advantages of the hybrid neural network (HNN) and quantile regression (QR). In the approach, the high dimensional input data are reorganized into a feature graph that is ready for feature extraction by the HNN. And the advantages of HNN can therefore be utilized to extract the representative features and construct nonlinear regression models. Meanwhile, by following the QR rules, the model can obtain quantiles and perform probabilistic prediction. By properly addressing the explanatory variable selection issue, the approach provides a specific solution for regional wind generation probabilistic prediction with huge input information. Test results on a region with 10 wind farms demonstrate the effectiveness of the proposed approach.

With the increase of solar photovoltaic (PV) permeability in power systems, high-precision PV power forecasting is critical to secure the economic operation of the smart grid. Solar irradiance is the most important factor affecting PV power. Accurately estimating the amount of solar irradiance leads to better production estimates from solar panels. Meanwhile, satellite images provide information on current and future cloud coverage, and are potentially useful in inferring solar irradiance. To accurately capture the effect of cloud on irradiance, this article develops a real-time mapping model between satellite image and solar irradiance. Based on the mapping model, a new ultrashort-term global horizontal irradiance (GHI) forecasting method combining satellite visible images and meteorological information is proposed. First, a modified method is utilized for reducing the influence of the solar zenith angle on pixel intensity and extracting the features in the image processing stage. Then, the cloud cover factors are extracted from satellite visible images by using the modified convolutional neural network. After that, the GHI forecasting model is developed, which is based on the combined use of meteorological information and cloud cover factors. Meanwhile, a cloud motion forecasting method using predicted wind speeds is developed for discussion. The better performance of the proposed method is demonstrated by comparing it with several benchmark models.

Yixiao Yu

Papers

Photovoltaic power forecast based on satellite images considering effects of solar position

A deep reinforcement learning method for managing wind farm uncertainties through energy storage system control and external reserve purchasing

Probabilistic Prediction of Regional Wind Power Based on Spatiotemporal Quantile Regression

Probabilistic Prediction of Regional Wind Power Based on Spatiotemporal Quantile Regression

Hybrid Solar Forecasting Method Using Satellite Visible Images and Modified Convolutional Neural Networks