基于Informer的负荷及光伏出力系数预测

doi:10.3969/j.issn.2097-0706.2024.04.008

摘要/Abstract

摘要：

为了支持可再生能源的布局及装机容量规划，长时间序列的电力负荷和光伏出力系数预测至关重要。光伏出力系数反映了光伏发电系统实际运行中的发电效率，但由于难以准确预测来年的气象信息，每日最大光伏出力系数的预测具有挑战性。为了克服这个限制，提出了利用每7 d计算1次的每日最大光伏出力系数的最大值和最小值构建一个包络线，通过预测该包络线的上限和下限，提供每日最大光伏出力系数可能的区间。这种包络线建模方式有助于在克服气象信息不确定性的同时提供更为鲁棒和可靠的预测结果。选用Informer模型作为预测框架，并与Transformer，LSTM和RNN模型进行了比较。基于实际电力负荷数据序列和光伏出力系数包络线上、下限数据序列进行仿真试验，验证了Informer模型的可行性和良好的预测精度。

关键词: 可再生能源, 负荷预测, 光伏出力系数, Informer模型, 长时间序列预测, 装机容量规划

Abstract:

A long-term time series forecast on electric loads and photovoltaic output coefficients is crucial for the layout and installed capacity design of renewable energy. A photovoltaic output coefficient can reflect the power-generating efficiency of a photovoltaic system in operation. But the inaccurate prediction on meteorological information brings challenges to the prediction on the maximum daily photovoltaic output coefficient. To overcome the problem，an envelope is constructed based on the maximum and minimal daily photovoltaic output coefficients which are calculated every 7 days. The upper and lower limits of the envelope provide a range of the factor. The envelope can overcome the uncertainties of meteorological information， so as to facilitate the prediction to be more robust and reliable. The Informer model employed as the prediction framework in this study is compared with Transformer， LSTM， and RNN models. A simulation test is carried out on a data sequence of actual electric loads and the upper and lower bounds of the photovoltaic output coefficients' envelope. The results validate the feasibility and outstanding prediction accuracy of the Informer model.

Key words: renewable energy, load prediction, photovoltaic output coefficient, Informer model, long-term time series forecasting, installed capacity planning

中图分类号:

TK01：TM615

缪月森, 夏红军, 黄宁洁, 李云, 周世杰. 基于Informer的负荷及光伏出力系数预测[J]. 综合智慧能源, 2024, 46(4): 60-67.

MIAO Yuesen, XIA Hongjun, HUANG Ningjie, LI Yun, ZHOU Shijie. Prediction on loads and photovoltaic output coefficients based on Informer[J]. Integrated Intelligent Energy, 2024, 46(4): 60-67.

图/表 17

图1

图2

表1

皮尔逊相关系数分类

类别	极低相关	低相关	中度相关	强相关	极强相关
$r x y$	[0,0.2)	[0.2,0.4)	[0.4,0.6)	[0.6,0.8)	[0.8,1.0]

表1

图3

图4

表2

时间编码

日期	时间戳
2016-12-01	[4；1；336]
2016-12-02	[5；2；337]
$⋮$	$⋮$
2022-11-30	[3；30；334]

表2

图5

图6

图7

表3

图8

图9

图10

表4

表5

表6

表7

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参数	数值
批次	32
编码器/解码器层数	4/4
注意力头数	8
编码器/解码器输入长度	96/413
优化器	Adam

模型	δ_MSE	δ_MAE	R²
Transformer	0.252	0.373	0.697
LSTM	0.468	0.551	0.435
RNN	0.474	0.555	0.299
Informer	0.204	0.333	0.756

模型	δ_MSE	δ_MAE	R²
Transformer	0.077	0.220	0.910
LSTM	0.108	0.335	0.781
RNN	0.213	0.378	0.725
Informer	0.075	0.203	0.923

模型	δ_MSE	δ_MAE	R²
Transformer	0.439	0.485	0.321
LSTM	0.641	0.600	0.266
RNN	0.639	0.606	0.178
Informer	0.408	0.467	0.504