基于Spearman系数和TCN的光伏出力超短期多步预测

ULTRA-SHORT-TERM PHOTOVOLTAIC POWER MULTI-STEP PREDICTION BASED ON SPEARMAN COEFFICIENT AND TCN

摘要: 研究一种基于Spearman相关系数和改进时间卷积网络（TCN）的超短期多步光伏功率预测方法。首先，采用Spearman相关系数方法对输入的天气特征量进行筛选；然后，构建合适的时间卷积网络使其适配光伏功率预测问题。经过实际的光伏电站数据测试，单步预测模型拟合度为99.41%，预测平均绝对误差为61.04，均优于传统的长短期记忆神经网络（LSTM）。

Abstract: This paper developed a ultra-short-term photovoltaic power prediction model based on Spearman coefficient and improved TCN. First,we used Spearman coefficient to screen the input weather characteristics,and then we built a proper TCN to make it suitable for the photovoltaic power prediction problems. Through the actual data test of photovoltaic power station,the fitting degree of the model is 99.41%,and MAE of prediction is 61.04,which is better than the traditional time series problem model LSTM.