"A Survey of Wind Power Ramp Forecasting"
written by Tinghui Ouyang, Xiaoming Zha, Liang Qin,
published by Energy and Power Engineering, Vol.5 No.4B, 2013
has been cited by the following article(s):
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[3] Environmental extreme events detection: A survey
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[7] Wind power ramp event detection with a hybrid neuro-evolutionary approach
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[8] Ordinal Multi-class Architecture for Predicting Wind Power Ramp Events Based on Reservoir Computing
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[9] Ensemble time series forecasting with applications in power systems and financial markets
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[10] Smoothing ramp events in wind farm based on dynamic programming in energy internet
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[11] New Hybrid Neuro-Evolutionary Algorithms for Renewable Energy and Facilities Management Problems
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[12] Alerting to Rare Large-Scale Ramp Events in Wind Power Generation
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[13] Short-term wind power ramp forecasting with empirical mode decomposition based ensemble learning techniques
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[14] Model of selecting prediction window in ramps forecasting
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[15] Robust estimation of wind power ramp events with reservoir computing
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[16] Improving the prediction of wind power ramps using texture extraction techniques applied to atmospheric pressure fields
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[17] A Hybrid Neuro-Evolutionary Algorithm for Wind Power Ramp Events Detection
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[18] Combining Reservoir Computing and Over-Sampling for Ordinal Wind Power Ramp Prediction
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[19] Wind power variation identification using ramping behavior analysis
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[20] Wind Power Ramps Driven by Windstorms and Cyclones
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[21] Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data
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[22] 风电功率爬坡事件作用下考虑时序特性的系统风险评估
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[23] Optimization of Time Window Size for Wind Power Ramps Prediction
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[24] Data Mining via Association Rules for Power Ramps Detected by Clustering or Optimization
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[25] Risk assessment of wind power ramp events based on prospect theory
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[26] Multiclass Prediction of Wind Power Ramp Events Combining Reservoir Computing and Support Vector Machines
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[27] Impacto da circulação atmosférica nas rampas de produção eólica em Portugal
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[28] Ensemble time series forecasting with applications in renewable energy
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[29] ARIMA based statistical approach to predict wind power ramps
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[30] Dictionary learning for short-term prediction of solar PV production
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[31] Improving and enhancing NWP based wind power forecasts under Norwegian conditions
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[32] Wind power prediction interval estimation method using wavelet-transform neuro-fuzzy network
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[33] Detecting Wind Power Ramp with Random Vector Functional Link (RVFL) Network
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[34] Previsão de produção eólica com modelização de incertezas
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[35] Risk Assessment of Wind Power Ramp Event Considering Time-sequence Characteristic
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[36] The role of flexibility in generation expansion planning of power systems with a high degree of renewables & vehicle electrification
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