Residential energy consumption prediction method based on residential user activity mode

Abstract

The invention discloses a residential energy consumption prediction method based on a residential user activity mode. The method comprises two steps of identification and classification of activity behaviors and energy consumption modes and building energy consumption prediction based on living activity modes.The identification and classification of the activity behavior and the energy consumptionmode extract the social and economic characteristics of the common characteristics of the living activity data set and the residential energy consumption data set, and the social and economic characteristics of the living activity data set and the residential energy consumption data set are respectively clustered to obtain the corresponding relationship between the corresponding cluster and the corresponding crowd; the activity mode and the building energy consumption mode are analyzed in the corresponding cluster, and the mapping between the interaction mode and the building energy consumption mode is mined; activity behavior modes and energy consumption mode categories to which the residents belong are identified through classification; and building energy consumption prediction is performed based on the living activity mode, firstly a residence type is simulated, then the activity mode and the residence type are fused, meteorological data characteristics are fused, and building energy consumption prediction is performed based on an engineering calculation method.

Description

technical field [0001] The invention relates to a residential energy consumption prediction method, in particular to a residential energy consumption prediction method based on residential user activity patterns, and belongs to the technical field of energy consumption prediction applications. Background technique [0002] my country's building energy consumption accounts for 16.2% of the world's total building energy consumption, second only to the United States, ranking second in the world, and building energy consumption accounts for about 30% of the total domestic social energy consumption. Building energy consumption is affected by various factors such as building structure, outdoor environment and occupant behavior, so it is difficult to predict building energy consumption. In the case of overestimation, construction and maintenance costs will not only increase significantly, but In the case of underestimation, the system may not provide enough energy to meet the comfo...

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Problems solved by technology

[0006] 1. For a specific building, accurate prediction of building energy consumption requires building structure data sets, residential behavior data sets, meteorological data sets, building energy consumption data sets, etc., but these data sets may be composed of multiple different These data provide information related to building energy consumption from different perspectives, and the information provided by a single data set is far from sufficient

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