Probabilistic Multi-Energy Load Forecasting for Integrated Energy System Based on Bayesian Transformer Network

概率逻辑 计算机科学 贝叶斯概率 水准点(测量) 贝叶斯网络 联合概率分布 概率分布 变压器 先验概率 编码器 动态贝叶斯网络 贝叶斯推理 人工智能 数据挖掘 工程类 数学 电压 操作系统 大地测量学 电气工程 统计 地理
作者
Chen Wang,Ying Wang,Zhetong Ding,Kaifeng Zhang
出处
期刊:IEEE Transactions on Smart Grid [Institute of Electrical and Electronics Engineers]
卷期号:15 (2): 1495-1508 被引量:9
标识
DOI:10.1109/tsg.2023.3296647
摘要

Probabilistic multi-energy load forecasting in an integrated energy system is very complex, because it needs to consider the following three aspects simultaneously: 1) Complex coupling relationship exists between multi-energy loads. 2) The intrinsic distribution of load uncertainties and dynamic changes of the distributions should be captured. 3) The probability distribution containing sufficient information should be generated. To address these issues, this paper proposes a multi-task Bayesian neural network, Bayesian Multiple-Decoder Transformer (BMDeT), which can capture both epistemic and aleatoric uncertainty, and achieve the joint probabilistic forecasting of the multi-energy loads considering their complex coupling relationship and related uncertainties. Firstly, the proposed model adopts the one-encoder multi-decoder framework, which could catch the multi-load coupling information by one Bayesian encoder and perform respective subtasks by multiple Bayesian decoders. Specifically, the Bayesian multi-head attention mechanism is proposed to capture the complex coupling relationship and uncertainties between multi-energy loads by optimizing the distribution of network parameters. Then, a multi-task balance method based on Bayesian theory is proposed to quantify the uncertainties of different tasks by giving trainable weights. Finally, the proposed model has been verified on a real-world load data set, the results show that it has superior performance over other benchmark models.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
hh发布了新的文献求助10
1秒前
3秒前
lc完成签到,获得积分10
5秒前
8秒前
李健的小迷弟应助anna采纳,获得10
9秒前
量子星尘发布了新的文献求助10
9秒前
11秒前
11秒前
嘀嘀咕咕发布了新的文献求助10
11秒前
大观天下完成签到,获得积分10
11秒前
科研通AI2S应助科研通管家采纳,获得10
12秒前
英姑应助科研通管家采纳,获得10
12秒前
科研通AI5应助科研通管家采纳,获得10
12秒前
12秒前
科研通AI2S应助科研通管家采纳,获得10
12秒前
共享精神应助科研通管家采纳,获得10
12秒前
科目三应助科研通管家采纳,获得10
12秒前
bkagyin应助科研通管家采纳,获得10
12秒前
SciGPT应助科研通管家采纳,获得10
12秒前
CodeCraft应助科研通管家采纳,获得10
12秒前
orixero应助科研通管家采纳,获得10
13秒前
脑洞疼应助科研通管家采纳,获得10
13秒前
13秒前
兴奋千兰发布了新的文献求助10
14秒前
有机发布了新的文献求助10
15秒前
yukang发布了新的文献求助10
15秒前
17秒前
大观天下发布了新的文献求助30
18秒前
18秒前
20秒前
21秒前
小盘子完成签到,获得积分10
21秒前
22秒前
今后应助务实的大神采纳,获得10
22秒前
anna发布了新的文献求助10
25秒前
25秒前
Elaine完成签到,获得积分10
25秒前
27秒前
nolan完成签到 ,获得积分10
27秒前
29秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989069
求助须知:如何正确求助?哪些是违规求助? 3531351
关于积分的说明 11253589
捐赠科研通 3269939
什么是DOI,文献DOI怎么找? 1804851
邀请新用户注册赠送积分活动 882074
科研通“疑难数据库(出版商)”最低求助积分说明 809073