Ranking of energy consumption objects using the principal components method

Abstract

Ensuring comfortable conditions in civil buildings requires the implementation of tasks of monitoring and forecasting the cost of energy resources, as well as energy-efficient management of heating engineering systems and its equipment. The implementation of appropriate automation and monitoring solutions allows the accumulation of a significant amount of data. To increase the informativeness of the analysis of energy efficiency in the operation of civil buildings a model of their information ranking was developed using correlation analysis and the principal component analysis. Based on the interdisciplinary methodology of data analysis (CRISP-DM), the basic indicators were determined for the accepted initial conditions on electricity and heat consumption of the university buildings and the matrix of correlation coefficients of their interrelation was estimated. Certain data (external volume and area of the building and average temperature values for this region according to the norm) are obtained from the technical documentation of buildings and available from open sources, others (amount of consumed heat and electricity, indoor temperature) are determined during operation and characterize the efficiency of energy resources in the building. At the initial stage, a correlation analysis of the relationship between the main parameters that characterize buildings and their consumption of energy resources. The principal component analysis was used to reduce the dimensionality of the feature set of data and to identify homogeneous groups of energy consumption objects. The obtained four components explain about 90% of the variance of the initial data and characterize the efficiency of energy use in terms of temperature, volume and coefficient of heating degree days of the heating season. The obtained results are recommended for implementation in modern systems of energy monitoring and municipal energy management as applied models for diagnosing abnormal situations and sound management decisions. Keywords – buildings; energy consumption; principal components; machine learning; data segmentation.