Probabilistic load prediction based on gated spiking neural P system model

doi:10.11930/j.issn.1004-9649.202505075

Abstract

Abstract:

Conventional deterministic load forecasting fails to provide uncertainty information of loads, while probabilistic load forecasting can generate probability distributions of predicted value uncertainty, thus providing comprehensive information for power grid dispatch decisions. In order to further improve the accuracy of probabilistic load forecasting, this paper proposes a model, which incorporates the least absolute shrinkage and selection operator (LASSO) and gated spiking neural P system (GSNP). Firstly, LASSO is used to extract the key features from external features such as minimum temperature, maximum temperature, average temperature, average humidity and precipitation. Subsequently, an improved GSNP model is developed to implement probabilistic load forecasting, enhancing the performance of long-term time-series forecasting. The case study using two long-term time-series datasets at different scales shows that the proposed model outperforms several other typical models in terms of both prediction accuracy and prediction interval quality.

Key words: probabilistic load forecasting, least absolute shrinkage and selection operator, deep neural network, quantile regression, gated spiking neural P system

SUI Zeyuan, WANG Jun, PENG Hong, WANG Delin, SONG Ge. Probabilistic load prediction based on gated spiking neural P system model[J]. Electric Power, 2026, 59(5): 46-56.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202505075

https://www.electricpower.com.cn/EN/Y2026/V59/I5/46

Figures/Tables 17

References 30

1	KONG X Y, LI C, ZHENG F, et al. Improved deep belief network for short-term load forecasting considering demand-side management[J]. IEEE Transactions on Power Systems, 2020, 35 (2): 1531- 1538.
2	张恒, 郑建勇, 梅飞, 等. 基于VMD和辅助任务学习的短期负荷预测方法[J]. 电力系统保护与控制, 2025, 53 (5): 104- 112.
	ZHANG Heng, ZHENG Yongjian, MEI Fei, et al. Short-term load forecasting method based on VMD and auxiliary task learning[J]. Power System Protection and Control, 2025, 53 (5): 104- 112.
3	和萍, 刘鑫, 宫智杰, 等. 高比例可再生能源电力系统源荷储联合调峰分层优化运行[J]. 电力系统保护与控制, 2024, 52 (18): 112- 122.
	HE Ping, LIU Xin, GONG Zhijie, et al. Hierarchical optimization operation model for joint peak-load regulation of source-load-storage in a high proportion of renewable energy power system[J]. Power System Protection and Control, 2024, 52 (18): 112- 122.
4	甘业平, 白云龙, 韩号, 等. 基于动态权重模型组合的短期区域净负荷预测方法[J]. 电力信息与通信技术, 2025, 23 (3): 17- 24.
	GAN Yeping, BAI Yunlong, HAN Hao, et al. Short-term regional net load forecasting method based on dynamic weight model combination[J]. Electric Power Information and Communication Technology, 2025, 23 (3): 17- 24.
5	谭智文, 徐茹枝, 关志涛. 基于差分隐私的个性化联邦电力负荷预测方案[J]. 电力信息与通信技术, 2024, 22 (7): 18- 26.
	TAN Zhiwen, XU Ruzhi, GUAN Zhitao. A personalized federal power load forecasting scheme based on differential privacy[J]. Electric Power Information and Communication Technology, 2024, 22 (7): 18- 26.
6	庞松岭, 赵雨楠, 唐金锐, 等. 基于充电桩利用率的充电负荷超短期预测方法研究[J]. 电力科学与技术学报, 2024, 39 (1): 115- 123.
	PANG Songling, ZHAO Yunan, TANG Jinrui, et al. A novel ultra short-term charging load forecasting method based on usage degree of charging piles[J]. Journal of Electric Power Science and Technology, 2024, 39 (1): 115- 123.
7	刘舒, 姚尚坤, 周敏, 等. 基于ICEEMDAN-TA-LSTM模型的主动配电网短期运行态势预测[J]. 电力科学与技术学报, 2023, 38 (6): 175- 186.
	LIU Shu, YAO Shangkun, ZHOU Min, et al. Active distribution network operating situation prediction based on ICEEMDAN-TA-LSTM model[J]. Journal of Electric Power Science and Technology, 2023, 38 (6): 175- 186.
8	苏向敬, 朱敏轩, 宇海波, 等. 基于频谱注意力和无交叉联合分位数回归的海上风电功率超短期概率预测[J]. 电力系统保护与控制, 2024, 52 (21): 103- 116.
	SU Xiangjing, ZHU Minxuan, YU Haibo, et al. Ultra-short-term probabilistic forecasting of offshore wind power based on spectral attention and non-crossing joint quantile regression[J]. Power System Protection and Control, 2024, 52 (21): 103- 116.
9	XU C L, SUN Y J, DUA A, et al. Quantile regression based probabilistic forecasting of renewable energy generation and building electrical load: A state of the art review[J]. Journal of Building Engineering, 2023, 79, 107772.
10	万灿, 崔文康, 宋永华. 新能源电力系统概率预测: 基本概念与数学原理[J]. 中国电机工程学报, 2021, 41 (19): 6493- 6508.
	WAN Can, CUI Wenkang, SONG Yonghua. Probabilistic forecasting for power systems with renewable energy sources basic concepts and mathematical principles[J]. Proceedings of the CSEE, 2021, 41 (19): 6493- 6508.
11	赵洪山, 吴雨晨, 温开云, 等. 基于时空注意力机制的台区多用户短期负荷预测[J]. 电工技术学报, 2024, 39 (7): 2104- 2115.
	ZHAO Hongshan, WU Yuchen, WEN Kaiyun, et al. Short-term load forecasting for multiple customers in a station area based on spatial-temporal attention mechanism[J]. Transactions of China Electrotechnical Society, 2024, 39 (7): 2104- 2115.
12	ZHANG D, WANG S, LIANG Y. A novel combined model for probabilistic load forecasting based on deep learning and improved optimizer[J]. Energy, 2023, 264, 126172.
13	赵佩, 代业明. 基于实时电价和加权灰色关联投影的SVM电力负荷预测[J]. 电网技术, 2020, 44 (4): 1325- 1332.
	ZHAO Pei, DAI Yeiming. Power load forecasting of SVM based on real-time price and weighted grey relational projection algorithm[J]. Power System Technology, 2020, 44 (4): 1325- 1332.
14	RUNGE J, SALOUX E. A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system[J]. Energy, 2023, 269, 126661.
15	DANG S, PENG L, ZHAO J, et al. A quantile regression random forest-based short-term load probabilistic forecasting method[J]. Energies, 2022, 15 (2): 663.
16	ZHANG W, HE Y, YANG S. Day-ahead load probability density forecasting using monotone composite quantile regression neural network and kernel density estimation[J]. Electric Power Systems Research, 2021, 201, 107551.
17	邵必林, 纪丹阳. 基于VMD-SE的电力负荷分量的多特征短期预测[J]. 中国电力, 2024, 57 (4): 162- 170.
	SHAO Bilin, JI Danyang. Multi-feature short-term prediction of power load components based on VMD-SE[J]. Applied Energy, 2024, 57 (4): 162- 170.
18	杨胡萍, 余阳, 汪超, 等. 基于VMD-CNN-BIGRU的电力系统短期负荷预测[J]. 中国电力, 2022, 55 (10): 71- 76.
	YANG Huping, YU Yang, WANG Chao, et al. Short-term load forecasting of power system based on VMD-CNN-BIGRU[J]. Electric Power, 2022, 55 (10): 71- 76.
19	LUO L, DONG J, KONG W, et al. Short-term probabilistic load forecasting using quantile regression neural network with accumulated hidden layer connection structure[J]. IEEE Transactions on Industrial Informatics, 2024, 20 (4): 5818- 5828.
20	ZHANG W, QUAN H, SRINIVASAN D. An improved quantile regression neural network for probabilistic load forecasting[J]. IEEE Transactions on Smart Grid, 2019, 10 (4): 4425- 4434.
21	王炜, 冯斌, 黄刚, 等. 基于自注意力编码器和深度神经网络的短期净负荷预测[J]. 中国电机工程学报, 2023, 43 (23): 9072- 9084.
	WANG Wei, FENG Bin, HUANG Gang, et al. Short-term net load forecasting based on self-attention encoder and deep neural network[J]. Proceedings of the CSEE, 2023, 43 (23): 9072- 9084.
22	李科, 潘庭龙, 许德智. 基于MSCNN-BiGRU-Attention的短期电力负荷预测[J]. 中国电力, 2025, 58 (6): 10- 18.
	LI Ke, PAN Tinglong, XU Dezhi. Short-term power load forecasting based on MSCNN-BiGRU-Attention[J]. Electric Power, 2025, 58 (6): 10- 18.
23	ANEIROS G, NOVO S, VIEU P. Variable selection in functional regression models: A review[J]. Journal of Multivariate Analysis, 2022, 188, 104871.
24	TIBSHIRANI R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society, 1996, 58 (1): 267- 288.
25	LU S, XU Q, JIANG C, et al. Probabilistic load forecasting with a non-crossing sparse-group lasso-quantile regression deep neural network[J]. Energy, 2022, 242, 122955.
26	LIU S M, CHEN H W, LIU P X, et al. A novel electricity load forecasting based on probabilistic least absolute shrinkage and selection operator-quantile regression neural network[J]. International Journal of Hydrogen Energy, 2023, 48, 34486- 34500.
27	LIU Q, LONG L, PENG H, et al. Gated spiking neural P systems for time series forecasting[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34 (9): 6227- 6236.
28	IONESCU M, PAUN G, YOKOMORI T, et al. Spiking neural P systems[J]. Fundamenta Informaticae, 2006, 71 (2-3): 279- 308.
29	EMMERT-STREIB F, DEHMER M. High-dimensional LASSO-based computational regression models: regularization, shrinkage, and selection[J]. Machine Learning and Knowledge Extraction, 2019, 1 (1): 359- 383.
30	陈轩伟. 基于BP-QR模型的负荷区间预测[J]. 电气技术, 2022, 23 (4): 14- 17.
	CHEN Xuanwei. Load interval forecasting based on BP-QR model[J]. Electrical Engineering, 2022, 23 (4): 14- 17.

模型	参数
模型共同参数	滞后值：1，神经元个数：24，学习率：0.001，训练轮数：25，优化器：Adam，激活函数：RuLU
CNN	卷积层：1，卷积核数量：32，卷积核大小：1
LASSO	正则化权重：0.001

模型	参数
模型共同参数	滞后值：1，神经元个数：24，学习率：0.001，训练轮数：25，优化器：Adam，激活函数：RuLU
CNN	卷积层：1，卷积核数量：32，卷积核大小：1
LASSO	正则化权重：0.001

特征	相关度/%
最低温度	69.20
最高温度	17.72
平均温度	12.21
平均湿度	0.14
降雨量	0.71

特征	相关度/%
最低温度	69.20
最高温度	17.72
平均温度	12.21
平均湿度	0.14
降雨量	0.71

置信度/%	本文方法			GSNP			LSTM			GRU			CBLM			CBGU
置信度/%	X_PICP	X_PINAW	X_AIS	X_PICP	X_PINAW	X_AIS	X_PICP	X_PINAW	X_AIS	X_PICP	X_PINAW	X_AIS	X_PICP	X_PINAW	X_AIS	X_PICP	X_PINAW	X_AIS
90	91.57	0.0835	–519.18	92.93	0.1110	–690.49	89.57	0.1126	–700.37	96.14	0.2104	–1307.95	96.00	0.1570	–976.17	94.45	0.1283	–797.67
85	86.79	0.0744	–473.50	88.71	0.0997	–631.03	87.71	0.1007	–670.23	93.93	0.1621	–1127.10	90.75	0.1381	–859.26	90.80	0.1179	–733.42
80	81.71	0.0602	–332.79	85.00	0.0806	–445.15	78.50	0.0797	–440.48	83.07	0.1265	–698.76	81.90	0.1149	–714.61	83.50	0.1033	–642.51