引用本文: 李琛, 郭文利, 吴进, 金晨曦. 基于BP神经网络的北京夏季日最大电力负荷预测方法[J]. 气候与环境研究, 2019, 24(1): 135-142.
Chen LI, Wenli GUO, Jin WU, Chenxi JIN. A Method for Prediction of Daily Maximum Electric Loads in the Summer in Beijing Based on the BP Neural Network[J]. Climatic and Environmental Research, 2019, 24(1): 135-142.
 Citation: Chen LI, Wenli GUO, Jin WU, Chenxi JIN. A Method for Prediction of Daily Maximum Electric Loads in the Summer in Beijing Based on the BP Neural Network[J]. Climatic and Environmental Research, 2019, 24(1): 135-142.

## A Method for Prediction of Daily Maximum Electric Loads in the Summer in Beijing Based on the BP Neural Network

• 摘要: 利用2006~2017年北京夏季（6~8月）逐日最大电力负荷和同期气象资料，分析最大电力负荷与各种气象因子的相关性，基于BP（Back Propagation）神经网络算法，建立了两种夏季日最大电力负荷预测模型并对比。结果表明：北京夏季周末基础负荷远小于工作日，剔除时应加以区分；气象因子对气象负荷的影响具有累积效应，累积2 d时两者的相关性最强；结合实际，根据自变量的不同分别建立了两种日最大电力负荷预测模型；经实际预测检验，两种预测模型均取得了较好的预测效果，能够满足电力部门的实际需求，其中自变量中加入前一日气象负荷的模型效果更优。

Abstract: Based on daily maximum electric loads and meteorological data in the summer (June-August) from 2006 to 2017 in Beijing, the relationship between electric load and meteorological factors is diagnosed. Using the BP (Back Propagation) neural network algorithm, two maximum electric power load prediction models are established and evaluated. The results indicate that (1) the basic electric load on weekends in Beijing in the summer is much less than that in working days, which should be distinguished when being removed; (2) the influence of meteorological factors on meteorological load has cumulative effect, and the correlation between them is the highest for two days of accumulation; (3) taking the actual situation into account, two different daily maximum electric load forecasting models are established based on different independent variables. Comparing the prediction results with actual data, both of the forecasting models show good prediction performance that can meet the actual demand of the power sector. The forecasting model with meteorological load of the previous day as an independent variable shows better prediction effect.

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