Very-Short-Term Power Prediction for PV Power Plants Using a Simple and Effective RCC-LSTM Model Based on Short Term Multivariate Historical Datasets

Biaowei Chen,Zhicong Chen,Yunfeng Lai,Lijun Wu,Peijie Lin,Shuying Cheng

doi:10.3390/electronics9020289

Abstract

Improving the accuracy of very-short-term (VST) photovoltaic (PV) power generation prediction can effectively enhance the quality of operational scheduling of PV power plants, and provide a reference for PV maintenance and emergency response. In this paper, the effects of different meteorological factors on PV power generation as well as the degree of impact at different time periods are analyzed. Secondly, according to the characteristics of radiation coordinate, a simple radiation classification coordinate (RCC) method is proposed to classify and select similar time periods. Based on the characteristics of PV power time-series, the selected similar time period dataset (include power output and multivariate meteorological factors data) is reconstructed as the training dataset. Then, the long short-term memory (LSTM) recurrent neural network is applied as the learning network of the proposed model. The proposed model is tested on two independent PV systems from the Desert Knowledge Australia Solar Centre (DKASC) PV data. The proposed model achieving mean absolute percentage error of 2.74–7.25%, and according to four error metrics, the results show that the robustness and accuracy of the RCC-LSTM model are better than the other four comparison models.

Highlights

Developing renewable energy can effectively reduce dependence on fossil energy and other burning energy sources, thereby improving the world’s energy and economic security [1,2]
Due to the power output of the PV power generation system is largely affected by environmental factors, the economic benefits of the PV plant depend on the flexibility of PV power systems [9]
Compared to the linear methods, the nonlinear approaches can notably improve the accuracy of forecasting since the ability of adaptability and self-update

Summary

Introduction

Developing renewable energy can effectively reduce dependence on fossil energy and other burning energy sources, thereby improving the world’s energy and economic security [1,2]. According to the latest data [8], the global installed capacity of new PV has reached. The newly installed capacity of PV in China, US, Japan and Germany reached 45 GW, 10.6 GW, 6.5 GW, and 3.0 GW, respectively. Due to the power output of the PV power generation system is largely affected by environmental factors, the economic benefits of the PV plant depend on the flexibility of PV power systems [9]. In order to improve the flexibility of the demand side and supply side in the PV market, increasing the resolution and accuracy of PV Power generation predictions becomes critical and urgent [10]

Methods

Results

Discussion

Conclusion