一种基于IGWO-SNN的光伏出力短期预测方法

光伏出力短期预测对于电网或微电网的能量管理和优化调度具有重要意义。构建了一种基于改进灰狼学习算法(improved grey wolf optimization, IGWO)的脉冲神经网络(spiking neural network, SNN)，并将其应用到光伏出力短期预测中。首先，利用灰色关联分析法选取相似日。然后，提出一种IGWO算法用于SNN模型训练，通过引入基于三角函数规律变化的非线性下降收敛因子和动态权重更新策略，提升SNN的编码和预测的性能。最后，利用实证系统对所提方法进行了评估，并与其他3种模型进行了对比研究。结果表明，所提方法预测性能提升明显。

An IGWO-SNN-based method for short-term forecast of photovoltaic output

Short-term forecasting of photovoltaic (PV) output is of great significance for energy management and optimal scheduling of power grids or microgrids. A spiking neural network (SNN) based on an improved grey wolf optimization (IGWO) algorithm is constructed and applied to short-term forecasting of PV output. First, the grey relation analysis method is used to select similar days. Then, an IGWO algorithm is proposed for SNN model training, and the performance of SNN coding and forecasting is improved by introducing a nonlinear descent convergence factor and updating strategy of dynamic weight based on the regular changes of trigonometric functions. Finally, the performance of the proposed method is evaluated using a demonstration system, and compared with three other models. The results show that the proposed method significantly improves prediction performance. This work is supported by Natural Science Foundation of Zhejiang Province (No. LY20E070004).