基于特征筛选的综合能源系统多元负荷日前-日内预测

doi:10.3969/j.issn.2097-0706.2024.03.006

摘要/Abstract

摘要：

负荷预测是指导综合能源系统调度与运行的前提。为更加经济高效地实施系统日前计划、日内优化，提出一种基于特征筛选的多元负荷日前-日内预测方法。首先，结合特征工程中3类特征筛选方法筛选预测模型输入特征，简化模型的同时能够保存下最重要的特征，针对日前-日内预测策略分别确立输入特征集；然后通过多任务学习硬共享机制，采用长短期记忆神经网络建立预测模型，实现不同子任务信息共享，并通过随机搜索方法优化网络参数以提高预测精度；最后以北京某产业园区供暖季电、热负荷为案例进行分析，日前、日内预测综合精度分别达到91.3%和95.2%。分析结果表明，该预测方法能够为系统日前调度和日内运行优化提供良好支撑，且预测结果优于未经特征筛选预测和单独负荷预测，证明了该预测方法具有更高的预测精度。

关键词: 综合能源系统, 多元负荷, 特征筛选, 日前-日内预测, 多任务学习, 长短期记忆神经网络

Abstract:

Load forecasting is a prerequisite for guiding the scheduling and operation of integrated energy systems（IES）. In order to carry out day-ahead scheduling and intra-day operation optimization on IES more economically and efficiently， a multi-load day-ahead and intra-day forecasting method based on feature screening is proposed. Firstly， by combining three types of feature screening methods in feature engineering，input features of forecasting models are selected. The combining method simplifies the models while preserving the important features， and the input feature sets for day-ahead and intra-day forecasting models are selected respectively. Then， taking hard parameter sharing in multi-task learning， the forecasting models are established based on long short-term memory neural network， achieving information sharing among different subtasks. And the forecasting accuracies of the models are optimized through random search method. Finally， taking an industrial park in Beijing as a study case，its energy system's electricity and heat loads are analyzed，and the comprehensive accuracies of the day-ahead and intra-day forecasting reach 91.3% and 95.2%，respectively. The method provides a sound support for IES day-ahead scheduling and intra-day operation optimization. Compared with the results of forecasting without feature screening and the forecasting on a single load， the method proposed has a higher forecasting accuracy.

Key words: integrated energy system, multi-load, feature screening, day-ahead and intra-day forecasting, multi-task learning, long short-term memory neural network

中图分类号:

TK01

徐聪, 胡永锋, 张爱平, 由长福. 基于特征筛选的综合能源系统多元负荷日前-日内预测[J]. 综合智慧能源, 2024, 46(3): 45-53.

XU Cong, HU Yongfeng, ZHANG Aiping, YOU Changfu. Multi-load day-ahead and intra-day forecasting for integrated energy systems based on feature screening[J]. Integrated Intelligent Energy, 2024, 46(3): 45-53.

图/表 19

图1

图2

图3

表1

LSTM神经网络参数

参数	含义	参数	含义
$x t$	当前时刻输入值	$h t$	当前时刻隐藏层输出
$f t$	遗忘门控制信号	$h t - 1$	上一时刻隐藏层输出
$i t$	输入门控制信号	$C t$	当前时刻细胞状态
$o t$	输出门控制信号	$C t - 1$	前一时刻细胞状态
$C t ~$	信息候选状态

表1

图4

图5

表2

表3

表4

表5

图6

图7

图8

图9

表6

图10

图11

表7

表8

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项目	e	h	t	d	p	v	w
e	1.000	0.367	0.498	-0.242	-0.060	0.082	0.520
h	0.367	1.000	-0.348	-0.490	0.202	0.038	0.197

排名	递归特征消除法		XGBoost梯度提升树法
排名	e	h	e	h
1	w	d	w	d
2	t	t	t	t
3	p	w	v	p
4	d	p	p	v
5	v	v	d	w

参数名称	设定范围	参数名称	设定范围
epochs	10~200（间隔为10）	batch_size	4，8
num_layers	1，2，3	gamma	0.5
hidden_size	16，32，64，128	step_size	5

参数名称	值	参数名称	值
epochs	100	lr	0.1
num_layers	2	batch_size	8
hidden_size	64

指标	电负荷	热负荷	综合指标
MAE/kW	233.7	478.2	356.0
MAPE	0.084	0.090	0.087
δ/%	91.6	91.0	91.3