The prediction of the impact of climatic factors on short-term electric power load based on the big data of smart city

2nd Annual International Conference on Information System and Artificial Intelligence

物理学;计算机科学

Qiu, Yunfei^1 ; Li, Xizhong^1 ; Zheng, Wei^1 ; Hu, Qinghe^2 ; Wei, Zhanmeng^1 ; Yue, Yaqin^3

National Grid Yingkou Company, East 40 Bohai Street, Zhanqian Destrict, Yingkou

115000, China^1

College of Information Science and Enginering, Northeastern University, No. 3-11, Wenhua Road, Heping District Shenyang

110819, China^2

Software College, Northeastern University, No.195, Chuangxin Road, Hunnan District Shenyang

110169, China^3

关键词: Accurate prediction;    Climatic factors;    Daily temperatures;    Electric power;    Electricity-consumption;    Forecasting modeling;    Neural network toolboxes;    Prediction methods;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012023/pdf
DOI  :  10.1088/1742-6596/887/1/012023

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

The climate changes have great impact on the residents' electricity consumption, so the study on the impact of climatic factors on electric power load is of significance. In this paper, the effects of the data of temperature, rainfall and wind of smart city on short-term power load is studied to predict power load. The authors studied the relation between power load and daily temperature, rainfall and wind in the 31 days of January of one year. In the research, the authors used the Matlab neural network toolbox to establish the combinational forecasting model. The authors trained the original input data continuously to get the internal rules inside the data and used the rules to predict the daily power load in the next January. The prediction method relies on the accuracy of weather forecasting. If the weather forecasting is different from the actual weather, we need to correct the climatic factors to ensure accurate prediction.