基于CEEMD与GWO-LSTM的精细非侵入性负荷监测

doi:10.3969/j.issn.2097-0706.2023.11.005

摘要/Abstract

摘要：

针对集中供热系统滞后性等因素导致的短期热负荷预测精度较低问题，提出一种基于互补集合经验模态分解（CEEMD）数据处理技术与灰狼优化算法-长短期记忆网络（GWO-LSTM）的精细非侵入性负荷监测方法。采用CEEMD将采集得到的原始热负荷数据分解为若干个平稳的固有模态函数（IMF）并对每个IMF分别进行建模预测，叠加每个IMF的预测值作为最终的预测输出。为了提高预测精度，利用GWO算法对LSTM的隐藏层神经元数目、训练次数和初始学习率进行参数寻优，建立CEEMD-GWO-LSTM短期热负荷预测模型。以实际热负荷数据进行仿真试验并与单一LSTM模型、CEEMD-LSTM模型进行对比，试验结果表明，CEEMD-GWO-LSTM模型的均方根误差、平均绝对误差和平均绝对百分比误差分别为0.591 5 MW，0.460 2 MW和8.083 8%，显著低于其他对比模型。

关键词: 集中供热系统, 短期热负荷预测, 非侵入性负荷监测, 长短期记忆网络, 灰狼优化算法, 互补集合经验模态分解

Abstract:

Aiming at the low accuracy of short-term thermal load prediction caused by the lag of central heating system and other factors， a fine non-invasive load monitoring method based on Complementary Ensemble Empirical Mode Decomposition （CEEMD） data processing technology and Gray Wolf Optimization and Long Short-Term Memory （GWO-LSTM） network is proposed. The collected raw data about heat load are decomposed into several stationary intrinsic mode functions （IMFs） by CEEMD， and each IMF is modelled and predicted separately. Then，the final prediction result is obtained by superimposing the predicted values of each IMF. To improve the prediction accuracy， GWO is used to optimize the number of neurons in the hidden layer， the number of training times， and the initial learning rate of the LSTM， then the CEEMD-GWO-LSTM prediction model for short-term heat load is established. Simulation experiments were conducted on the simple LSTM model and CEEMD-LSTM model. The experimental results showed that the root mean square error（RMSE）， mean absolute error（MAE）， and mean absolute percentage error （MAPE）of the prediction made by CEEMD-GWO-LSTM model were 0.591 5 MW， 0.460 2 MW， and 8.083 8%， respectively， which were significantly lower than that of other models.

Key words: central heating system, short-term thermal load prediction, non-invasive load monitoring, long and short-term memory network, Grey Wolf Optimization algorithm, complementary ensemble empirical mode decomposition

中图分类号:

TK11:TP399

陆潍潍, 殷林飞. 基于CEEMD与GWO-LSTM的精细非侵入性负荷监测[J]. 综合智慧能源, 2023, 45(11): 36-44.

LU Weiwei, YIN Linfei. Fine non-invasive load monitoring based on CEEMD and GWO-LSTM[J]. Integrated Intelligent Energy, 2023, 45(11): 36-44.

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时间		平均热负荷	时间		平均热负荷
2016-11-01	09:00	5.7	2016-12-12	04:00	3.8
	10:00	5.8		05:00	3.9
	11:00	5.5		06:00	4.1
	12:00	5.9		07:00	4.4
	13:00	6.3		08:00	4.3
	14:00	8.9		09:00	4.5
	15:00	10		10:00	4.3
	16:00	9.8		11:00	5.4
	17:00	10.8		12:00	5.2
	18:00	8.7		13:00	7.1
	19:00	8.1		14:00	5.8
	20:00	9.9		15:00	5.3
	21:00	8.5		16:00	6.9
	22:00	6.3		17:00	6.7
	23:00	5.3		18:00	6.5
…		…		19:00	5.2
2016-12-12	00:00	6.0		20:00	5.5
	01:00	5.9		21:00	4.9
	02:00	4.3		22:00	4.0
	03:00	4.1		23:00	3.5

参数	数值
灰狼种群个数	20
最大迭代次数	20
优化维度	3
LSTM隐藏层神经元数目i	[1, 200]
训练次数j	[1, 200]
LSTM初始学习率μ	[0.001,0.010]

参数	数值
输入特征维数	1
隐藏层神经元数目	100
全连接层数	1
初始学习率	0.003
学习率更新周期	125
学习率更新因子	0.2
求解器	Adam

分量	隐藏层节点数	训练次数	初始学习率
I₁	1.336 5	200.000 0	0.009 9
I₂	93.398 8	168.932 7	0.006 8
I₃	200.000 0	146.320 2	0.009 0
I₄	92.562 4	200.000 0	0.009 5
I₅	133.801 8	200.000 0	0.010 0
I₆	33.181 6	172.798 0	0.009 5
I₇	14.607 7	129.445 4	0.004 3
I₈	8.866 5	186.174 9	0.001 6
I₉	175.654 9	124.115 3	0.009 5
R	49.579 2	33.243 0	0.008 0

模型	δ_RMSE/MW	δ_MAE/MW	δ_MAPE/%
LSTM	1.198 5	0.958 8	16.317 8
CEEMD-LSTM	1.161 0	0.767 6	13.033 6
CEEMD-GWO-LSTM	0.591 5	0.460 2	8.083 8