基于Spearman系数和TCN的光伏出力超短期多步预测

吴珺玥; 赵二刚; 郭增良; 张亚萍; 张建军

doi:10.19912/j.0254-0096.tynxb.2022-0654

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (9) : 180-186. DOI: 10.19912/j.0254-0096.tynxb.2022-0654

基于Spearman系数和TCN的光伏出力超短期多步预测

吴珺玥^1~3, 赵二刚^1~3, 郭增良⁴, 张亚萍⁴, 张建军^1~3

作者信息 +

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

Wu Junyue^1-3, Zhao Ergang^1-3, Guo Zengliang⁴, Zhang Yaping⁴, Zhang Jianjun^1-3

Author information +

文章历史 +

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.

导出引用

吴珺玥, 赵二刚, 郭增良, 张亚萍, 张建军. 基于Spearman系数和TCN的光伏出力超短期多步预测[J]. 太阳能学报. 2023, 44(9): 180-186 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0654

Wu Junyue, Zhao Ergang, Guo Zengliang, Zhang Yaping, Zhang Jianjun. ULTRA-SHORT-TERM PHOTOVOLTAIC POWER MULTI-STEP PREDICTION BASED ON SPEARMAN COEFFICIENT AND TCN[J]. Acta Energiae Solaris Sinica. 2023, 44(9): 180-186 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0654

中图分类号： TM615

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