Online recognition and filling method for hourly energy consumption abnormality data of office building

Abstract

The invention discloses an online recognition and filling method for the hourly energy consumption abnormality data of an office building, and the method comprises the following steps: obtaining historical energy consumption data; clearing the historical energy consumption data, and eliminating the abnormality data; building an energy consumption mode feature set according to the outdoor temperature, moment and date attributes of the hourly energy consumption data, and calculating a feature value; collecting the energy consumption data hour by hour, carrying out the energy consumption mode matching, and finding a corresponding feature value; carrying out the online recognition of abnormal data; carrying out the cyclic interpolation of the abnormality data, and carrying out the correction of the interpolation value through a formula; storing the data without abnormality into a database after interpolation; carrying out the rolling correction of the feature values of the energy consumption mode regularly, and then carrying out the online interpolation of the hourly energy consumption data. The method can solve a problem of the recognition and interpolation of the usual abnormality data in the hourly energy consumption of the office building, improves the energy consumption data quality, provides effective data for the further data mining, and promotes the development of the energy saving operation of the building.

Description

technical field [0001] The invention relates to the technical field of energy consumption data preprocessing, in particular to an online identification and filling method for hourly energy consumption abnormal data of office buildings. Background technique [0002] Since 2007, my country has successively formulated and promulgated a series of policies and regulations and corresponding technical guidelines, and carried out the construction of energy consumption supervision system for office buildings of state agencies and large public buildings. Provinces and municipalities have established networked dynamic energy consumption monitoring platforms for office buildings of state agencies and large public buildings. Energy saving in office buildings is an important part of building a green and smart city. Office building energy consumption data is the basis for office building energy consumption benchmark assessment, energy use evaluation, and energy-saving renovation, and its d...

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Problems solved by technology

Because these three interpolation methods are based on the existing real data for formula interpolation, the interpolation effect is directly related to the existing real data volume. When the missing data is continuous and the missing data volume is large, the real data volume is relatively small, and the correlation Weak, general interpolation effect

The period method interpolation is based on the hidden period attribute of periodic data. The hidden period attribute is mainly the statistically significant hidden period in the sequence, and the corresponding period average and peak value. However, due to the hidden hourly energy consumption data of office buildings The cycle number is relatively stable and the range of change is small, so the advantage of the cycle method interpolation is not obvious, and it cannot reflect the actual energy consumption well

The intelligent data filling method based on the energy consumption model adopts the fine classification and statistical analysis of the energy consumption model of office buildings to obtain the corresponding eigenvalues ​​of the energy consumption data, and on this basis, the continuous missing data is interpolated. This method is more in line with the actual energy consumption. However, this method divides the energy consumption pattern into 144 categories, which does not fully consider the characteristics of the energy consumption pattern of office buildings, and the number of classifications of energy consumption patterns has a greater impact on the data interpolation effect, and this method is only applicable to historical energy consumption data Data imputation, unable to achieve online imputation of missing data

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