Abstract
As photovoltaic (PV) power plants are an essential component of modern smart grids, the PV generation forecasting of such plants has recently been gaining interest. The forecasting results of PV power often suffer from large errors because of unusual weather conditions. In a learning-based forecasting model, the forecasting accuracy can be enhanced by using carefully selected data for training rather than all the data without any screening. That is, using a training set that only contains information obtained from similar days can help enhance the accuracy of learning-based PV forecasting. This paper proposes a forecasting method for small-scale PV generation. This method is based on long short-term memory; further, it detects similar days considering the different impacts of weather variables on PV power according to the day. This method can address issues caused by unnecessary learning from non-similar historical days. The simulation results demonstrate that the proposed method exhibits better performance than do existing similar day detection methods.
Highlights
Photovoltaic (PV) power has attracted significant attention as an emission-free power source owing to increasing awareness about global warming
For the cases without the proposed similar day detection, forecasting models based on Support vector regression (SVR), Back-propagation neural networks (BPNNs), and long short-term memory (LSTM) were tested, proposed similar day detection, forecasting models based on SVR, BPNN, and LSTM were tested, and and each network was trained via back-propagation with mean squared error as the loss function
In this paper we proposed a forecasting method for small-scale PV generation based on LSTM
Summary
Photovoltaic (PV) power has attracted significant attention as an emission-free power source owing to increasing awareness about global warming. The common goal for all these hybrid techniques is to seek similar days from historical data and, eventually, to create a new PV series for better training results These methodologies have an average forecasting nMAE of around 5–18%, merely depending upon a type of day. To enhance the accuracy of learning-based PV forecasting, a proper training set should be developed that only contains information obtained from similar days considering the different impacts of weather variables on PV power for each day type. This paper proposes a similar day detection (SDD) method for learning-based PV forecasting that deals with different impacts of weather variables on PV power for each day type. A new PV series is created from the identified similar days, which is more repetitive and appropriate for the LSTM-based PV forecasting model
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