准确预测光伏电站输出功率，是促进光伏并网发电，提高电网运行稳定性的主要途径之一。该文提出一种基于粒子群算法－最小二乘支持向量机(particle swarm optimization and least squares support vector machine，PSO-LSSVM)的日前光伏功率预测方法，该方法首先利用粒子群算法的全局搜索能力来获取最小二乘支持向量机的惩罚因子和核函数宽度，有效解决了最小二乘支持向量机难以快速精准寻找最优参数的问题；然后利用数值天气预报和光伏功率的历史数据对PSO-LSSVM模型进行训练，利用训练好的PSO-LSSVM模型对日前光伏功率进行预测。对比分析PSO-LSSVM模型与长短期记忆网络(long short-term memory，LSTM)模型、最小二乘支持向量机(least squares support vector machine，LSSVM)、PSO-BP模型的预测结果可知：PSO-LSSVM模型的预测精度高于LSTM模型、LSSVM模型和结合粒子群的BP神经网络(particle swarm optimization and back propagation, PSO-BP)模型，证明了所提PSO-LSSVM预测模型的优越性。

Accurate prediction of output power of photovoltaic power plant is one of the main ways to promote grid connected photovoltaic power generation and improve the stability of power grid operation. This paper proposed a day-ahead photovoltaic power forecasting method based on particle swarm optimization and least squares support vector machine (PSO-LSSVM). This method used the global search ability of PSO algorithm to obtain the penalty factor and kernel function width of LSSVM, which effectively solved the problem that LSSVM is difficult to find the optimal parameters quickly and accurately. Then, the historical data of numerical weather prediction (NWP) and photovoltaic power were used to train the PSO-LSSVM model, and the trained PSO-LSSVM model was used to forecast the day-ahead photovoltaic power. The forecasting results of PSO-LSSVM, long short-term memory(LSTM), least squares support vector machine(LSSVM) and PSO-BP models were compared and analyzed, and the comparison results show that the forecasting accuracy of PSO-LSSVM model is higher than those of LSTM, LSSVM and particle swarm optimization and back propagation (PSO-BP) models, which proves the superiority of the PSO-LSSVM forecasting model.