基于监督式学习的自适应竞价预测模型研究

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (10256 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Aimed at the characteristics of competitive bidding in the early stage of electricity market, which is less data available for research, the environment changes greatly and the user behavior is uncertain, a set of strategies for adaptive bidding supervised learning algorithm based on time series is proposed. The strategy combines the traditional mechanical learning methods, current electricity market rules and user behavior characteristics, the forgetting mechanism is used to simulate the maturing market behavior of users and the inertia mechanism is used to simulate the delayed response of users to the market. The self-verification mechanism is designed to correct the unreasonable forecasting errors. The regularization parameters avoid overfitting. The simulation results of the experimental example predict the supply-side curve and the demand-side curve, and verify the correctness and effectiveness of the proposed study based on the clearing result of the Guangdong electricity market.

Key words： self-adaption supervised learning electricity market bidding

Received: 09 March 2018 Published: 23 October 2018

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Chu Rihui
	Hu Qinran
	Shi Xiang
	Li Peng

Cite this article:

Chu Rihui,Hu Qinran,Shi Xiang等. Study on adaptive bidding prediction algorithm based on supervised learning[J]. Electrical Engineering, 2018, 19(10): 1-5.

URL:

https://dqjs.cesmedia.cn/EN/Y2018/V19/I10/1

[1] 曾鸣. 能源互联网与售电市场放开[J]. 中国电力企业管理, 2015(13): 54-57.
[2] 梁刚, 王中荣, 马占军, 等. 基于售电侧放开的电力市场交易研究[J]. 管理观察, 2016(31): 32-35.
[3] 何国中, 史军. 电力市场售电侧运营机制综述[J]. 电气技术, 2017, 18(12): 15-18.
[4] 周明, 严宇, 丁琪, 等. 国外典型电力市场交易结算机制及对中国的启示[J]. 电力系统自动化, 2017(20): 1-8, 150.
[5] 曾鸣, 周健, 于滢, 等. 国外电力改革对我国电力零售市场建设的启示[J]. 改革与战略, 2009, 25(4): 179-182.
[6] 陈晨, 董晓天, 管文林, 等. 新电改形势下地区供电公司服务模式与售电业务研究[J]. 电气技术, 2017, 18(10): 5-12.
[7] Carriόn M, Philpott A B, Conejo A J, et al.A stochastic programming approach to electric energy procurement for large consumer[J]. IEEE Trans. Power Systems, 2007, 22(2): 744-754.
[8] Hatami A R, Seifi H, Sheikh-El-Eslami M K. Optimal selling price and energy procurement for a retailer in an electricity market[J]. Electric Power Systems Research, 2009, 79(1): 246-254.
[9] Contreras J, Espinola R, Nogales F J, et al.ARIMA models to predict next-day electricity prices[J]. Power Systems IEEE Transactions on, 2003, 18(3): 1014-1020.
[10] Conejo A J, Plazas M A, Espinola R, et al.Day-ahead electricity price forecasting using the wavelet transform and ARIMA models[J]. IEEE Transactions on Power Systems, 2005, 20(2): 1035-1042.
[11] 李玲玲, 马东娟, 李志刚. 触点动态接触电阻时间序列混沌预测[J]. 电工技术学报, 2014, 29(9): 187-193.
[12] 孙斌, 姚海涛. 风电场风速时间序列的多重分形去趋势波动分析[J]. 电工技术学报, 2014, 29(6): 204-210.
[13] 李乐. 电力市场背景下需求侧报价的时间序列分析方法研究[D]. 北京: 华北电力大学, 2016.
[14] 刘荣荣. 基于卷积神经网络的手写数字识别软件的设计与实现[D]. 呼和浩特: 内蒙古大学, 2015.
[15] 范磊, 卫志农, 李慧杰, 等. 基于变分模态分解和蝙蝠算法-相关向量机的短期风速区间预测[J]. 电力自动化设备, 2017, 37(1): 93-100.