摘要
电力市场改革初期,月度竞价由于可供研究的数据少、环境变化大、市场参与者行为不确定等特点难以分析预测。本文将监督式机械学习算法与当前电力市场规则和竞价者行为特征相结合,提出了一种具有自适应能力的竞价预测方法,引入遗忘机制和惯性机制来模拟真实市场参与者竞价行为,设计了自我验证机制修正不合理的预测结果,改进了正则化参数,有效避免了过拟合的发生。本文实验算例采用广东月度竞价的实验市场数据,验证了所提方法的有效性。
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.
作者
初日辉
胡秦然
时翔
李鹏
Chu Rihui;Hu Qinran;Shi Xiang;Li Peng(Nanjing SAC Power Grid Automation Co.,Ltd,Nanjing 211106;SEAS,Harvard University,Cambridge UK 02138;State Grid Qingdao Electric Power Company,Qingdao,Shandong 266002)
出处
《电气技术》
2018年第10期1-5,9,共6页
Electrical Engineering
基金
2017年江苏省工业和信息产业转型升级专项资金资助项目(苏经信综合[2017]378号)
关键词
自适应
监督式学习
电力市场
竞价
self-adaption
supervised learning
electricity market
bidding
作者简介
初日辉（1988-）,男,山东烟台人,硕士研究生,主要研究方向为园区能源互联网、售电运营管理平台.
