基于典型气象周的GRNN光伏发电量预测模型

由于光伏发电量具有波动性,且现有的光伏发电量预测技术存在气象因素考虑不全面、特征提取不充分等问题,为提高光伏发电量预测精度,文中提出一种改进的典型气象年方法(TMY Method)生成典型气象年数据,并结合广义回归神经网络(GRNN)进行光伏发电量预测。首先,选择6种历史气象指标,利用Finkelstein-Schafer统计方法选择典型气象周,并生成典型气象年数据;然后,使用因子分析法对会影响光伏发电量的气象指标进行筛选,对筛选出的气象指标和日光伏发电量进行标准化处理后,将其作为GRNN模型的初始输入量,得到预测日的光伏发电量;最后,利用江苏省南京市的历史气象数据及日发电量数据对所设计的模型进行训练和预测。结果表明,与标准TMY Method-GRNN预测方法相比,文中所提预测方法有较好的预测性能。

Photovoltaic power generation prediction model based on optimized TMY Method-GRNN

Because of the problems which the output power of photovoltaics is volatile and intermittent, and the existing photovoltaic power prediction technology has incomplete consideration of meteorological factors and insufficient feature extraction. In order to improve the accuracy of photovoltaic power generation prediction, this paper proposes an improved Typical Meteorological Year (TMY) Method to generate typical meteorological year data and combined with the Generalized Regression Neural Network to predict photovoltaic power generation. The specific steps are as follows: first of all, select six kinds of historical meteorological indicators, use Finkelstein-Schafer statistical method to select typical meteorological week and generate typical meteorological year data. Then, the Factor Analysis (FA) method is used to filter out the meteorological indicators that affect the photovoltaic power generation, and the selected meteorological indicators and daily photovoltaic power generation are standardized as the initial input of the GRNN model to obtain the predicted daily photovoltaic power generation. Finally, the designed model was trained and predicted using historical weather data and daily power generation data from Nanjing, Jiangsu Province. The results show that the prediction method proposed in this paper has better prediction accuracy than the original TMY-GRNN prediction method.#$NLKeywords: Typical Meteorological Year; GRNN; TMY Method; Photovoltaic power generation prediction