关于光伏保险发展的问题分析与对策建议

随着“双碳”目标的提出,我国以光伏为主的新能源结构转型正在持续推进。现今光伏发电站建设运营以及光伏产品生产过程中存在较大风险,迫切需要光伏保险保障。由于光伏行业发展时间较短,光伏保险发展较为缓慢,存在保险产品设计与实际需求不匹配,缺乏光伏保险数据积累,市场环境与市场监管有待完善,光伏企业投保意识薄弱等问题。要满足光伏行业发展需求,需扩大光伏保险承保范围,建立光伏风险评估机构,搭建相关数据平台,鼓励保险公司创新发展等,有效推动光伏保险健康发展。

基于LSTM-AR模型的光伏指数保险欺诈检测方法研究

光伏指数保险是一种基于太阳辐射总量的天气指数保险产品,旨在为光伏电站提供发电量不足的风险保障。然而,光伏指数保险中存在着因缺乏客观数据与评价标准而导致的保险欺诈问题。本文针对这一问题,提出了一种基于深度学习的模型,结合LSTM、注意力机制和残差连接,该模型使用太阳辐射量对光电企业发电量进行预测。为验证模型的有效性,本文使用2019年新疆光电光伏数据集进行测试并与传统的最小二乘法的回归预测进行比较。测试结果表明,本文所提出的模型可以有效使用太阳辐射量对光电企业发电量进行预测,进而帮助保险企业针对光伏企业的发电量有关的保险欺诈行为进行识别,为光伏保险业务的规范化和发展提供了一种有效的技术手段。Photovoltaic (PV) index insurance is a weather index insurance product based on the total amount of solar radiation, designed to provide risk protection against insufficient power generation for PV power plants. However, PV index insurance faces issues of insurance fraud due to the lack of objective data and evaluation standards. To address this problem, this paper proposes a deep learning-based model that combines LSTM, attention mechanism, and residual connections. This model uses solar radiation data to predict the power generation of photovoltaic enterprises. To validate the effectiveness of the model, the 2019 Xinjiang photovoltaic dataset was used for testing and compared with traditional least squares regression predictions. The test results show that the proposed model can effectively use solar radiation data to predict the power generation of photovoltaic enterprises, thereby helping insurance companies identify fraudulent activities related to power generation in PV enterprises. This provides an effective technical means for the standardization and development of PV insurance business.

浅析新时代下我国新能源保险的发展前景——以光伏发电保险为视角

随着保险业在我国的发展地位不断提高,以及保险法的修订和完善,保险业的发展逐步走向正轨。目前,全球关注的一大热点问题,即新能源的开发利用走进了人们的视线,但研发及应用过程中可能面临的巨额损失成了阻碍其发展的"绊脚石"。因此,新能源保险应运而生,如何将保险与新能源的发展紧密融合也有待深思。本文以光伏发电保险为视角,对我国新能源保险的发展略作解读。