微型电脑应用

由于太阳能受昼夜特性和气象不确定性的影响，光伏发电存在电能波动的问题，导致电能的输出不稳定。为了有效监测和准确评估光伏发电功率，提出基于时序回归的短期功率预测方法。采用灰狼优化(GWO)算法对4种预测算法进行优化，利用历史光伏发电功率数据和气象数据进行训练，建立光伏发电功率预测模型，实现数据驱动的光伏功率时序预测以及组串运行状态的评估。通过比较分析，验证了GWO-RFR预测模型的优异性能，同时为组串工作状态诊断提供了数据支撑。

Due to the intermittency and randomness of solar energy, photovoltaic power generation suffers from energy fluctuations, leading to unstable electricity output. In order to effectively monitor and accurately evaluate the photovoltaic power generation, a short-term power prediction method based on time series regression is proposed. The grey wolf optimization algorithm is used to optimize four prediction algorithms. Historical photovoltaic power generation data and meteorological data are used for training, and a photovoltaic power generation prediction model is established to achieve data-driven time series prediction and evaluation of string operation status. By comparative analysis, the excellent performance of the GWO-RFR prediction model is verified, and data support is provided for the diagnosis of the working status of the string.