結(jié)合貝葉斯Autoformer的多維自適應(yīng)短期電力負(fù)荷概率預(yù)測(cè)方法

周師琦; 王俊帆; 賴俊升; 袁毓杰; 董哲康

doi:10.11999/JEIT240398

結(jié)合貝葉斯Autoformer的多維自適應(yīng)短期電力負(fù)荷概率預(yù)測(cè)方法

doi: 10.11999/JEIT240398 cstr: 32379.14.JEIT240398

周師琦^{1, 2},
王俊帆^{1, 2},
賴俊升³,
袁毓杰^4, ,,
董哲康^{1, 2}

1.
杭州電子科技大學(xué)電子信息學(xué)院杭州 310018
2.
浙江省裝備電子重點(diǎn)實(shí)驗(yàn)室杭州 310018
3.
英國(guó)倫敦布魯奈爾大學(xué)電子與計(jì)算機(jī)工程系倫敦 UB8 3PH
4.
中國(guó)民航大學(xué)空中交通管理學(xué)院天津 300300

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(62206062)，長(zhǎng)三角科技創(chuàng)新共同體聯(lián)合攻關(guān)重點(diǎn)項(xiàng)目(2023CSJGG1300)，浙江省屬高?；究蒲袠I(yè)務(wù)費(fèi)項(xiàng)目(GK229909299001-06)

詳細(xì)信息

作者簡(jiǎn)介:
周師琦：女，博士生，研究方向?yàn)橹悄茈娋W(wǎng)、能源管理、深度學(xué)習(xí)

王俊帆：女，博士生，研究方向?yàn)槟茉垂芾?、深度學(xué)習(xí)

賴俊升：男，教授，研究方向?yàn)橹悄茈娋W(wǎng)、能源管理

袁毓杰：女，講師，研究方向?yàn)橹悄茈娋W(wǎng)、能源管理

董哲康：男，副教授，研究方向?yàn)槟茉垂芾怼⑸疃葘W(xué)習(xí)

通訊作者:
袁毓杰　18114026@bjtu.edu.cn

中圖分類號(hào): TN911.7; TM715; TP18
計(jì)量
- 文章訪問數(shù): 453
- HTML全文瀏覽量: 267
- PDF下載量: 50
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-05-21
- 修回日期: 2024-08-26
- 網(wǎng)絡(luò)出版日期: 2024-08-30
- 刊出日期: 2024-12-01

Multi-view Adaptive Probabilistic Load Forecasting Combing Bayesian Autoformer Network

ZHOU Shiqi^{1, 2},
WANG Junfan^{1, 2},
LAI Junsheng³,
YUAN Yujie^{4
, ,},
DONG Zhekang^{1, 2}

1.
School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Zhejiang Province Key Lab of Equipment Electronics, Hangzhou 310018, China
3.
Department of Electronic and Computer Engineering, Brunel University London, London UB8 3PH, UK
4.
School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

Funds: The National Natural Science Foundation of China (62206062), Yangtze River Delta Science and Technology Innovation Community Jointly Tackled Key Project (2023CSJGG1300), The Fundamental Research Funds for the Provincial University of Zhejiang (GK229909299001-06)

摘要

摘要: 建立準(zhǔn)確的電力負(fù)荷短期預(yù)測(cè)模型對(duì)于電力系統(tǒng)的穩(wěn)定運(yùn)行和智能化進(jìn)程至關(guān)重要。目前的主流預(yù)測(cè)方法無(wú)法很好地突破數(shù)據(jù)波動(dòng)性和模型不確定性兩個(gè)問題?；诖?，該文提出一種基于貝葉斯Autoformer的多維自適應(yīng)短期電力負(fù)荷概率預(yù)測(cè)方法。具體地，提出自適應(yīng)特征提取方法獲取多維度特征，通過捕捉多尺度特征和時(shí)頻局部信息，增強(qiáng)模型對(duì)負(fù)荷數(shù)據(jù)中高波動(dòng)性和非線性特征的處理能力。其次，提出基于貝葉斯Autoformer的預(yù)測(cè)模型，它可以捕獲負(fù)荷數(shù)據(jù)中重要子序列特征以及不確定性，并通過貝葉斯優(yōu)化方法實(shí)現(xiàn)概率預(yù)測(cè)分布和參數(shù)分布的動(dòng)態(tài)更新。所提模型在3個(gè)量級(jí)(GW, MW, KW)的實(shí)際負(fù)荷數(shù)據(jù)集上進(jìn)行一系列實(shí)驗(yàn)分析(對(duì)比分析、自適應(yīng)分析、魯棒性分析)。結(jié)果表明，所提預(yù)測(cè)模型在自適應(yīng)和準(zhǔn)確性方面具有優(yōu)越的性能，均方根誤差(RMSE)、彈球損失(Pinball Loss)、連續(xù)概率評(píng)分(CRPS)，相較對(duì)比方法分別提升1.9%, 24.2%, 4.5%。
- 負(fù)荷預(yù)測(cè) /
- 概率預(yù)測(cè) /
- 貝葉斯神經(jīng)網(wǎng)絡(luò) /
- Autoformer
Abstract: Establishing accurate short-term forecasting models for electrical load is crucial for the stable operation and intelligent advancement of power systems. Traditional methods have not adequately addressed the issues of data volatility and model uncertainty. In this paper, a multi-dimensional adaptive short-term forecasting method for electrical load based on Bayesian Autoformer network is proposed. Specifically, an adaptive feature selection method is designed to capture multi-dimensional features. By capturing multi-scale features and time-frequency localized information, the model is enhanced to handle high volatility and nonlinear features in load data. Subsequently, an adaptive probabilistic forecasting model based on Bayesian Autoformer network is proposed. It captures relationships of significant subsequence features and associated uncertainties in load time series data, and dynamically updates the probability prediction model and parameter distributions through Bayesian optimization. The proposed model is subjected to a series of experimental analyses (comparative analysis, adaptive analysis, robustness analysis) on real load datasets of three different magnitudes (GW, MW, and KW). The model exhibits superior performance in adaptability and accuracy, with average improvements in Root Mean Square Error (RMSE), Pinball Loss, and Continuous Ranked Probability Score (CRPS) of 1.9%, 24.2%, and 4.5%, respectively.
- Load forecasting /
- Probabilistic forecasting /
- Bayesian neural network /
- Autoformer

HTML全文

圖 1 電力負(fù)荷數(shù)據(jù)的波動(dòng)性分析

下載: 全尺寸圖片幻燈片

圖 2 電力負(fù)荷數(shù)據(jù)在不同時(shí)期的分布情況

下載: 全尺寸圖片幻燈片

圖 3 電力負(fù)荷數(shù)據(jù)的漂移檢測(cè)結(jié)構(gòu)

下載: 全尺寸圖片幻燈片

圖 4 基于貝葉斯 Autoformer 的多維自適應(yīng)電負(fù)荷概率預(yù)測(cè)模型

下載: 全尺寸圖片幻燈片

圖 5 超參數(shù)設(shè)置

下載: 全尺寸圖片幻燈片

圖 6 3 個(gè)電力負(fù)荷數(shù)據(jù)集在不同預(yù)測(cè)維度下概率預(yù)測(cè)的結(jié)果

下載: 全尺寸圖片幻燈片

圖 7 不同特征選擇方法下的預(yù)測(cè)結(jié)果

下載: 全尺寸圖片幻燈片

圖 8 不同預(yù)測(cè)維度下在線自適應(yīng)模型和離線模型的預(yù)測(cè)結(jié)果

下載: 全尺寸圖片幻燈片

表 1 模型的超參數(shù)設(shè)置

參數(shù)	值
編碼層數(shù)l_E	4
解碼層數(shù)l_D	4
多頭注意力h	8
模型維度d_M	24
滑動(dòng)窗口長(zhǎng)度l_W	168
滑動(dòng)窗口步長(zhǎng)d_W	1

下載: 導(dǎo)出CSV

表 2 不同預(yù)測(cè)模型在3 個(gè)數(shù)據(jù)集下的性能對(duì)比結(jié)果

方法	New England (GW)			ASU (MW)			400builds (KW)
方法	RMSE	Pinball	CRPS	RMSE	Pinball	CRPS	RMSE	Pinball	CRPS
FPSwq2Q^[10]	1.065 9	0.194 7	0.998 1	0.176 7	0.014 7³	0.207 1	0.069 1	0.019 6	0.059 2
QLSTM^[8]	0.985 7³	0.117 9²	0.692 4	0.179 3	0.016 2	0.200 7	0.062 3	0.013 7	0.061 9
*BNN^[15]	1.217 7	0.161 8	0.595 7³	0.182 2	0.016 7	0.195 9	0.140 8	0.015 9	0.094 2
*BSDeT^[14]	1.032 4	0.148 2	0.974 3	0.148 9³	0.013 7²	0.130 8²	0.058 2³	0.010 9³	0.045 2²
**MQR^[12]	1.527 3	0.216 9	0.631 0	0.174 9	0.016 8	0.201 5	0.091 6	0.020 7	0.072 8
**APLF^[13]	0.911 0²	0.132 8³	0.506 7²	0.140 6²	0.018 2	0.144 7³	0.032 2²	0.010 5²	0.048 1³
本文	0.910 9¹	0.078 0¹	0.495 1¹	0.139 4¹	0.012 4¹	0.120 1¹	0.031 4¹	0.010 3¹	0.023 7¹
注：上標(biāo)1,2,3分別表示排名第1、第2和第3；基于BNN的概率預(yù)測(cè)模型，*為自適應(yīng)概率預(yù)測(cè)模型

下載: 導(dǎo)出CSV

表 3 不同注意力機(jī)制用于貝葉斯概率預(yù)測(cè)的比較

方法	New England			ASU			400 builds
方法	RMSE	Pinball	CRPS	RMSE	Pinball	CRPS	RMSE	Pinball	CRPS
BNN+Full	0.943 5	0.082 5	0.790 5	0.190 8	0.015 1	0.147 6	0.032 5	0.010 9	0.024 9
BNN+Log	0.930 2	0.081 0	0.778 5	0.189 8	0.015 1	0.146 6	0.0322	0.0107	0.0244
BNN+In	0.943 5	0.079 5	0.763 5	0.190 3	0.014 9¹	0.146 2	0.031 7	0.011 2	0.024 1
BNN+Auto	0.915 0¹	0.078 0¹	0.742 5¹	0.188 9¹	0.014 9¹	0.144 2¹	0.031 4¹	0.010 3¹	0.023 9¹
注：上標(biāo)1表示排名第1。

下載: 導(dǎo)出CSV

表 4 概率預(yù)測(cè)模型的穩(wěn)定性分析

評(píng)價(jià)指標(biāo)		RMSE	Pinball	CRPS
New England	對(duì)照組	0.9150	0.0780	0.7425
New England	噪聲組	0.9157	0.0784	0.7431
ASU	對(duì)照組	0.1889	0.0149	0.1442
ASU	噪聲組	0.1890	0.0150	0.1446
400build	對(duì)照組	0.0314	0.0103	0.0239
400build	噪聲組	0.0318	0.0107	0.0240

下載: 導(dǎo)出CSV

參考文獻(xiàn)(24)

[1]	LAI C S, YANG Yuxiang, PAN Keda, et al. Multi-view neural network ensemble for short and mid-term load forecasting[J]. IEEE Transactions on Power Systems, 2021, 36(4): 2992–3003. doi: 10.1109/TPWRS.2020.3042389.
[2]	KIM N, PARK H, LEE J, et al. Short-term electrical load forecasting with multidimensional feature extraction[J]. IEEE Transactions on Smart Grid, 2022, 13(4): 2999–3013. doi: 10.1109/TSG.2022.3158387.
[3]	LI Jian, DENG Daiyu, ZHAO Junbo, et al. A novel hybrid short-term load forecasting method of smart grid using MLR and LSTM neural network[J]. IEEE Transactions on Industrial Informatics, 2021, 17(4): 2443–2452. doi: 10.1109/TII.2020.3000184.
[4]	DANG Sanlei, PENG Long, ZHAO Jingming, et al. A quantile regression random forest-based short-term load probabilistic forecasting method[J]. Energies, 2022, 15(2): 663. doi: 10.3390/en15020663.
[5]	王俊帆, 陳毅, 高明煜, 等. 智能交通感知新范式: 面向元宇宙的交通標(biāo)志檢測(cè)架構(gòu)[J]. 電子與信息學(xué)報(bào), 2024, 46(3): 777–789. doi: 10.11999/JEIT230357. WANG Junfan, CHEN Yi, GAO Mingyu, et al. A new paradigm for intelligent traffic perception: A traffic sign detection architecture for the metaverse[J]. Journal of Electronics and Information Technology, 2024, 46(3): 777–789. doi: 10.11999/JEIT230357.
[6]	WANG Bingzhi, MAZHARI M, and CHUNG C Y. A novel hybrid method for short-term probabilistic load forecasting in distribution networks[J]. IEEE Transactions on Smart Grid, 2022, 13(5): 3650–3661. doi: 10.1109/TSG.2022.3171499.
[7]	ZHANG Dongxue, WANG Shuai, LIANG Yuqiu, et al. A novel combined model for probabilistic load forecasting based on deep learning and improved optimizer[J]. Energy, 2023, 264: 126172. doi: 10.1016/j.energy.2022.126172.
[8]	WANG Yi, GAN Dahua, SUN Mingyang, et al. Probabilistic individual load forecasting using pinball loss guided LSTM[J]. Applied Energy, 2019, 235: 10–20. doi: 10.1016/j.apenergy.2018.10.078.
[9]	LUO Long, DONG Jizhe, KONG Weizhe, 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. doi: 10.1109/TII.2023.3341242.
[10]	FAUSTINE A and PEREIRA L. FPSeq2Q: Fully parameterized sequence to quantile regression for net-load forecasting with uncertainty estimates[J]. IEEE Transactions on Smart Grid, 2022, 13(3): 2440–2451. doi: 10.1109/TSG.2022.3148699.
[11]	LEMOS-VINASCO J, BACHER P, and M?LLER J K. Probabilistic load forecasting considering temporal correlation: Online models for the prediction of households’ electrical load[J]. Applied Energy, 2021, 303: 117594. doi: 10.1016/j.apenergy.2021.117594.
[12]	BRACALE A, CARAMIA P, DE FALCO P, et al. Multivariate quantile regression for short-term probabilistic load forecasting[J]. IEEE Transactions on Power Systems, 2020, 35(1): 628–638. doi: 10.1109/TPWRS.2019.2924224.
[13]	áLVAREZ V, MAZUELAS S, and LOZANO J A. Probabilistic load forecasting based on adaptive online learning[J]. IEEE Transactions on Power Systems, 2021, 36(4): 3668–3680. doi: 10.1109/TPWRS.2021.3050837.
[14]	YU Binbin, LI Jianjing, LIU Che, et al. A novel short-term electrical load forecasting framework with intelligent feature engineering[J]. Applied Energy, 2022, 327: 120089. doi: 10.1016/j.apenergy.2022.120089.
[15]	BRUSAFERRI A, MATTEUCCI M, SPINELLI S, et al. Probabilistic electric load forecasting through Bayesian mixture density networks[J]. Applied Energy, 2022, 309: 118341. doi: 10.1016/j.apenergy.2021.118341.
[16]	ISO NEW England[EB/OL]. https://www.iso-ne.com, 2024.
[17]	Metabolism[OL]. https://cm.asu.edu, 2023.
[18]	ANAO[OL]. https://www.anao.gov.au/work/performance-audit, 2023.
[19]	FEKRI M N, PATEL H, GROLINGER K, et al. Deep learning for load forecasting with smart meter data: Online adaptive recurrent neural network[J]. Applied Energy, 2021, 282: 116177. doi: 10.1016/j.apenergy.2020.116177.
[20]	FAN Guofeng, PENG Liling, and HONG W C. Short-term load forecasting based on empirical wavelet transform and random forest[J]. Electrical Engineering, 2022, 104(6): 4433–4449. doi: 10.1007/s00202-022-01628-y.
[21]	董哲康, 錢智凱, 周廣東, 等. 基于憶阻的全功能巴甫洛夫聯(lián)想記憶電路的設(shè)計(jì)、實(shí)現(xiàn)與分析[J]. 電子與信息學(xué)報(bào), 2022, 44(6): 2080–2092. doi: 10.11999/JEIT210376. DONG Zhekang, QIAN Zhikai, ZHOU Guangdong, et al. Memory circuit design, implementation and analysis based on Memristor full-function Pavlov associative[J]. Journal of Electronics and Information Technology, 2022, 44(6): 2080–2092. doi: 10.11999/JEIT210376.
[22]	CHEN Minghao, PENG Houwen, FU Jianlong, et al. AutoFormer: Searching transformers for visual recognition[C]. 2021 IEEE/CVF International Conference on Computer Vision, Montreal, Canada, 2021: 12270–12280. doi: 10.1109/ICCV48922.2021.01205.
[23]	CAO Defu, WANG Yujing, DUAN Juanyong, et al. Spectral temporal graph neural network for multivariate time-series forecasting[C]. Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2020: 1491.
[24]	LI Lei, XUE Zhigang, and DU Xiuquan. ASCRB: Multi-view based attentional feature selection for CircRNA-binding site prediction[J]. Computers in Biology and Medicine, 2023, 162: 107077. doi: 10.1016/j.compbiomed.2023.107077.