期刊
SUSTAINABILITY
卷 14, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/su14053092
关键词
photovoltaic systems; deep learning; transfer learning; energy generation; distributed generation; smart-grids
资金
- Catedra ELAND for Renewable Energies of the University of Jaen
- Spanish government [RTI2018-098979-A-I00]
- Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital, under the project Development of an architecture based on machine learning and data mining techniques for the prediction of indicators in the diagnosis and intervention of autism spect [AICO/2020/117]
This paper discusses the capabilities of deep learning models to predict energy generation in different regions and deployments, and analyzes the impact of transfer learning on energy prediction.
New trends of Machine learning models are able to nowcast power generation overtaking the formulation-based standards. In this work, the capabilities of deep learning to predict energy generation over three different areas and deployments in the world are discussed. To this end, transfer learning from deep learning models to nowcast output power generation in photovoltaic systems is analyzed. First, data from three photovoltaic systems in different regions of Spain, Italy and India are unified under a common segmentation stage. Next, pretrained and non-pretrained models are evaluated in the same and different regions to analyze the transfer of knowledge between different deployments and areas. The use of pretrained models provides encouraging results which can be optimized with rearward learning of local data, providing more accurate models.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据