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.
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