摘要
随着电网公司代理购电业务稳步推进,代理购电业务体系逐步完善,精确的代理购电用户用电量预测为保障电力安全稳定供应奠定了基础。因此,文章构建自适应权重组合模型,将不同校核方法的校核结果进行权重分配,从而提升校核结果准确性。首先,构建预测业务偏差校核流程框架,确定代理购电预测业务校核流程。然后分别选取分位数映射法、增量变化法以及支持向量回归(support vector regression,SVR)对预测结果进行校核,得到同一纬度下的不同方法校核结果。最后,建立遗传算法-优劣解距离法(genetic algorithm-technique for order preference by similarity to ideal solution,GA-TOPSIS)模型针对校核结果进行准确性与稳定性双目标优化,选取不同校核方法的最优权重组合。测试结果表明在校核方法权重组合校正后,相较于初始预测值和单一校核方法校核后的结果,预测精度和准确度得到明显提升。
With the steady advancement of the agent power purchase business of the grid company,the agent power purchase business system has been gradually improved,and the accurate prediction of power consumption of agent power purchase users lays the foundation for guaranteeing the safe and stable power supply.Therefore,this paper constructs an adaptive weight combination model to assign weights to the calibration results of different calibration methods,so as to improve the accuracy of the calibration results.First of all,the prediction business deviation calibration process framework is constructed to determine the agent power purchase prediction business calibration process.Then,the quartile mapping method,incremental change method and support vector regression(SVR)are selected to calibrate the prediction results,and the calibration results of different methods at the same latitude are obtained.Finally,a genetic algorithm-technique for order preference by similarity to ideal solution(GA-TOPSIS)model is established to optimize the accuracy and stability of the calibration results,and the optimal weight combinations of the different calibration methods are selected.The optimal weight combinations of different checking methods are selected.The test results show that the prediction accuracy and precision are significantly improved after the weight combination of the calibration methods,compared with the initial prediction value and the result after the single calibration method.
作者
魏立勇
周颖
李熠
邱敏
丁一
孙腾
李一鸣
WEI Liyong;ZHOU Ying;LI Yi;QIU Min;DING Yi;SUN Teng;LI Yiming(State Grid(Tianjin)Comprehensive Energy Services Co.,Ltd.,Binhai New Area,Tianjin 300456,China;China Electric Power Research Institute Co.,Ltd.,Haidian District,Beijing 100192,China;North China Electric Power University,Changping District,Beijing 102206,China;Electric Power Research Institute,State Grid Tianjin Electric Power Company,Xiqing District,Tianjin 300384,China)
出处
《电力信息与通信技术》
2025年第3期9-16,共8页
Electric Power Information and Communication Technology
基金
国网天津市电力公司科技项目资助“支撑代理购电新业务的用户分群电力电量精准预测关键技术研究与应用”(5400-202312226A-1-1-ZN)。
