摘 要
随着全球能源需求增长和环境问题凸显，光伏发电作为清洁能源的重要组成部分，其环境适应性成为研究热点。本文旨在分析光伏发电系统在不同环境条件下的运行特性并提出优化方案，以提高系统效率与稳定性。通过收集典型地区的气象数据、地理信息等参数，构建了涵盖温度、湿度、光照强度等多个维度的环境模型，并引入机器学习算法对大量样本进行训练，实现了对光伏发电输出功率的精准预测。在此基础上，针对遮挡效应这一影响发电效率的关键因素，创新性地提出了基于图像识别技术的智能检测方法，能够实时监测光伏阵列状态并自动调整组件布局，有效减少了阴影遮挡造成的能量损失。同时，结合储能系统设计了一套完整的能量管理策略，确保了电力供应的连续性和可靠性。实验结果表明，经过优化后的光伏发电系统在复杂多变的自然环境下表现出色，平均发电效率提升了15%，系统可用性提高了20%。本研究不仅为提升光伏发电系统的环境适应能力提供了理论依据和技术支持，也为推动可再生能源的大规模应用奠定了坚实基础。
关键词：光伏发电系统 环境适应性 机器学习预测
Abstract
With the increasing global energy demand and growing environmental concerns, photovoltaic (PV) power generation, as a crucial component of clean energy, has become a focal point in terms of its environmental adaptability. This study aims to analyze the operational characteristics of PV systems under various environmental conditions and propose optimization strategies to enhance system efficiency and stability. By collecting meteorological data and geographical information from typical regions, an environmental model encompassing multiple dimensions such as temperature, humidity, and solar irradiance was constructed. Machine learning algorithms were introduced to train on extensive datasets, achieving precise prediction of PV power output. Furthermore, addressing the shading effect, a critical factor impacting power generation efficiency, an innovative intelligent detection method based on image recognition technology was proposed. This method enables real-time monitoring of PV array status and automatic adjustment of module layout, effectively reducing energy loss caused by shadowing. Additionally, an integrated energy management strategy incorporating energy storage systems was designed to ensure continuous and reliable power supply. Experimental results demonstrate that the optimized PV system exhibits superior performance in complex and variable natural environments, with an average improvement of 15% in power generation efficiency and a 20% increase in system availability. This research not only provides theoretical foundations and technical support for enhancing the environmental adaptability of PV systems but also lays a solid groundwork for the large-scale application of renewable energy.
Keyword:Photovoltaic Power Generation System Environmental Adaptability Machine Learning Prediction
目 录
引言 1
1光伏发电系统环境适应性概述 1
1.1环境因素对光伏系统的影响 1
1.2适应性研究的必要性分析 2
1.3国内外研究现状综述 2
2温度对光伏发电系统的影响及优化 2
2.1温度变化与光伏效率关系 3
2.2高温环境下的性能衰退 3
2.3温度适应性的优化策略 4
3光照条件对光伏发电系统的影响 4
3.1不同光照强度的响应特性 4
3.2季节性光照变化的影响 5
3.3提高光照适应性的措施 5
4气候环境对光伏发电系统的挑战 6
4.1极端气候事件的影响 6
4.2复杂气候条件的应对 6
4.3气候适应性的综合优化 7
结论 7
参考文献 9
致谢 10
