Beijing China; School of Control Science and Engineering North China Electric Power UniverInternational conference on smart grid and clean energy technologiesWang, X., Guo, P. and Huang, X. (2011), "A review of wind power forecasting models," Energy Procedia, 12, 770-778....
review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the ... D Gayo-Avello - 《Social Science Computer Review》 被引量: 189发表: 2012年 Ensemble methods for wind and solar power forecasting-A state...
wind forecasting compared to other models (Abedinia et al., 2020a); the WT-based method was used to decompose the original signal to sub-signals, and two-dimensional convolution neural network (TDCNN) was taken to model these sub-signals to output wind power (Abedinia et al., 2020b). ...
The uncertainty and regularity of wind power generation are caused by wind resources' intermittent and randomness. Such volatility brings severe challenges to the wind power grid. The requirements for ultrashort-term and short-term wind power forecasting with high prediction accuracy of the model used...
power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day's wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data ...
forecastingmodelingrandom forestsstabilitywind powerWe focus on wind power modeling using machine learning techniques. We show on real data provided by the wind energy company Maa Eolis, that parametric models, even following closely the physical equation relating wind production to wind speed are ...
COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications 2023, Applied Energy Show abstract A review of solar hybrid photovoltaic-thermal (PV-T) collectors and systems 2023, Progress in Energy and Combustion Science Show abs...
Results show that the performance of ST-GWO-MSVM is better than other benchmark models in terms of multiple-error metrics including fractional bias, direction accuracy, and improvement percentages. 展开 关键词: Wind power forecasting Spatio-temporal correlation Multi-output support vector machine Grey ...
models aim to minimize the cost of energy (COE). However, the risk of the power deficiency could be high with these models because of the variations of wind conditions and electricity demands. This paper proposes a power-deficiency and risk-management (PDRM) model of micro-siting for ...
A review on the selected applications of forecasting models in renewable power systems 2019, Renewable and Sustainable Energy Reviews Show abstract Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage...