BP神经网络建立光伏发电预测模型 毕业论文.doc

杭州电子科技大学本科毕业设计 PAGE PAGE 44 摘要 随着社会的不断发展，传统能源的大量消耗使人们在工业发展和日常生活中面临关于不可再生能源耗尽和严重的环境污染等问题。太阳能作为一种优秀的可再生能源而受到世界各国的重视并具有较大发展潜力。随着光伏发电系统容量的不断扩大，准确地预测光伏系统未来几天的发电量对保证电网的稳定运行和大规模光伏发电系统的发展有着重要义。而城市建筑屋顶作为城市中利用率较低的部分，如果在闲置的屋顶上均安装太阳能光伏发电系统，对整个城市都将带来许多利益。本文提出了一种利用气象信息和历史发电量来预测次日光伏发电量的模型，整个模型采用非线性映射能力较强的BP神经网络来建立。原始数据由杭州电子科技大学光伏发电微网实验室提供，包括实验平台记录下的历史气象信息和对应当天的光伏发电量。由于原始数据有限，本文采用模块化的思想，先将模型按季节划分为春、夏、秋、冬四个子模型，再将每个季节模型按日气象类型划分为晴天、云天和雨天三个子模型，共计十二个子模型。以2010年10月的发电数据和气象数据为例，输入数据为预测前一日的光伏发电量和预测当日的温度和光照强度，对建立的神经网络进行训练，并对训练好的模型进行了测试、预测及评估。结果表明，预测模型的预测精度较高，对发电量的预测有较好的参考作用。最后结合杭州电子科技大学，查阅下沙校区的建筑物屋顶面积，推广到校园建筑物所有屋顶都安装上太阳能电池板，预测每日总发电量。 关键词：光伏发电量预测 气象因子 BP神经网络 模块化 ABSTRACT With the development of society, large consumption of traditional energy makes people face the problem of non-renewable energy depletion and serious environmental pollution in industrial development and daily life. As a predominant energy,solar energy has been paid attention to and will be a potential one new energy.With the increase of the capacity of PV system, forecast the generating capacity of PV systems in the next few days accurately has an important meaning to ensure the stable operation of electric grid and large-scale development of PV system. And the roof of city’s construction is unemployed. if the PV system are installed on these idle roof, it will bring much benefits to the city. This paper presents a prediction of the PV system model using the historical meteorological information and historical generation, BP neural network which has the ability of nonlinear mapping is used to establish the model. The original data is provided by photovoltaic micro-grid Laboratory of Hangzhou Dianzi University, which includes the historical meteorological information and the corresponding amount of the photovoltaic power generation. Because the original data is limited, this paper modularize the project .According to the fact of the season ,the first model is divided into four s