摘要
针对传统电力系统负荷预测算法数据预处理过程中大量的超参数调节以及数值强行代替标签时容易发生条件偏移的问题,为进一步提高电力系统短期负荷预测的精度,将CatBoost算法应用于电力短期负荷预测,较传统负荷预测深度神经网络算法而言,CatBoost算法在数据预处理过程当中无需进行数值强行代替标签,同时减小对超参数依赖的情况下得到较优的预测结果。首先选取了两地区20天的数据用于训练CatBoost模型,其次将预测结果与其他常见智能算法进行分析和对比,最后仿真结果表明CatBoost算法具备预测电力系统短期负荷的能力。
In order to improve the accuracy of short-term load forecasting in power systems, CatBoost algorithm is applied to short-term power load for prediction. Compared with the traditional load prediction deep neural network algorithm, the CatBoost algorithm does not need to perform numerical forced substitution in the data preprocessing process, and at the same time reduces the dependence on hyperparameters to obtain better prediction results. Firstly, 20 days of data from two regions are used to train the CatBoost model. Secondly, analyze the prediction results and compared with other common intelligent algorithms. Finally, the simulation results showed that the CatBoost algorithm has the ability to predict short-term load of the power system.
作者
党存禄
武文成
李超锋
李永强
DANG Cunlu;WU Wencheng;LI Chaofeng;LI Yongqiang(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050 China;Key Laboratory of Gansu Province Advanced Control for Industrial Processes,Lanzhou University of Technology,Lanzhou 730050 China;National Experimental Teaching Demonstration Center of Electrical and Control Engineering of Lanzhou University of Technology,Lanzhou 730050 China)
出处
《电气工程学报》
2020年第1期76-82,共7页
Journal of Electrical Engineering
基金
国家自然科学基金(51767017)
国网甘肃省电力科学研究院(SGGSKY00DJJS1900216)
甘肃省住房和城乡建设厅2018年建设科技基金项目[A1](JK2018-21)资助项目。
