Abstract
As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for forecasting wind power and PV production. The physical model, statistical learning method, and machine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production. Moreover, the experiments demonstrated that cloud map identification has a significant impact on PV generation. With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes, this paper summarizes the classification of wind power and PV generation systems, as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.
Highlights
As the energy crisis, environmental degradation, and climate change worsen, the usage of clean energy is becoming increasingly important
Wind power and solar power generation are widely employed in the power grid, and power system optimization is becoming increasingly important
The set order Long-Short-Term Memory (LSTM) method based on optimized hidden layer topology is emphatically introduced, which is utilized for short-term multivariable wind power forecasting
Summary
Environmental degradation, and climate change worsen, the usage of clean energy is becoming increasingly important. Short-term forecasting, or utilizing physical models based on numerical weather predictions and statistical models based on wind speed and wind power data for predicting, is the current trend in wind power forecasting. VGG, ResNet and other transfer learning models are used for cloud image classification and the hidden layer feature information of cloud images are used for classification analysis
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