大数据环境下区域建筑电力能耗快速预测仿真

Rapid Prediction of Electric Energy Consumption in Regional Buildings under Big Data Environment

大数据环境下区域建筑电力能耗的快速预测,对电力系统的发展具有重要意义。在对区域建筑的电力能耗进行预测时,需要对电力能耗数据进行分析,确定电力能耗的预测模型。传统方法主要是计算电力能耗的变量,导致准确性和有效性差的问题,提出基于时间序列分析的区域建筑电力能耗快速预测方法。根据区域建筑电力能耗数据,分析电力能耗的原始序列,采用平均加权的方式对电力能耗背景值进行计算,得到电力能耗微分方程,建立电力能耗灰色预测模型。以电力能耗的灰度预测模型为基础,利用电力能耗的季节因子、循环趋势和不规则因子对区域建筑电力能耗进行时间序列分析,获得电力能耗的时间权重值,完成大数据环境下区域建筑电力能耗快速预测。实现对区域建筑电力能耗的快速预测。仿真结果表明,提出方法在预测电力能耗时具有准确性和有效性。

The rapid prediction of regional building electricity consumption under big data environment is of great significance to the development of power system.When forecasting the power consumption of regional buildings,we need to analyze the power consumption data and determine the prediction model of power consumption.The traditional method is mainly to calculate the variable of power consumption,which leads to the poor accuracy and efficiency.This paper proposes a fast prediction method based on time series analysis for the energy consumption of regional buildings.According to the energy consumption data of regional buildings,the original sequence of power consumption is analyzed,the average weighting method is used to calculate the background value of the power consumption,the differential equation of the power consumption is obtained,and the grey prediction model of the power consumption is established.Based on the gray prediction model of power consumption,the time series analysis of the energy consumption of regional buildings is carried out by using the seasonal factor,circulation trend and irregular factor of the power consumption.The time weight value of the power consumption is obtained,and the rapid prediction of the energy consumption of the regional building under the big data environment is completed.The simulation results show that the proposed method is accurate and effective in predicting power consumption.