Zhongyao Du1,*, Xiaoying Chen1, Hao Wang2, Xuheng Wang1, Yu Deng1, Liying Sun1
Energy Engineering, Vol.119, No.4, pp. 1419-1438, 2022, DOI:10.32604/ee.2022.020283
- 23 May 2022
Abstract To attain the goal of carbon peaking and carbon neutralization, the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy. However, this approach is hindered by the lack of training data for predicting new grid-connected PV power stations. To overcome this problem, this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations. The proposed model integrates a structure model, heat-dissipation conditions, and the loss… More >
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