IOP Conference Series Earth and Environmental Science · 2021 · 22 citations · 4 references
ComputingComputer ScienceData ScienceForecastingMachine LearningTime Series AnalysisDeep LearningRenewable EnergyShort-term ForecastingTime Series Feature ExtractionTime Series ForecastingWind ForecastingDeep Learning For ForecastingDeep Learning For Time Series ForecastingDeep Learning For Wind ForecastingMachine Learning For Wind ForecastingPower ConsumptionShort-term Energy ForecastingTime Series Forecasting For WindWind Power Forecasting
Abstract Improving the accuracy of short-term wind power forecasting is critical to wind power consumption. This paper establishes a short-term wind power prediction model based on the multi-feature extraction and deep learning network CNN-LSTM. Muti-features are extracted from original data to improve the accuracy of training. In addition, clustering algorithm is used to classify training data and train the models corresponding to those classes. CNN-LSTM prediction models are established for each cluster and compared with ARIMA, RNN, CNN and LSTM models.
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Laurens van der Maaten, Geoffrey E. Hinton · Journal of Machine Learning Research · 2008 · 35.7K citations