Use of big data technologies for the implementation of energy-saving measures and renewable energy sources in buildings

Currently, one of the directions of energy saving in the industrial and social sectors of the Russian economy is the introduction of technically and economically feasible energy-saving measures and renewable energy sources (RES). However, it is often impossible to substantiate the effectiveness of introducing renewable energy sources reasonably. This is due to the fact that the comparison should be conducted in conditions of identical labor and raw materials resources, climatic conditions, etc. In other words, it is necessary to compare the energy saving measures and the use of renewable energy technologies under comparable conditions, which in many cases requires the solution of a number of complex problems. Based on the analysis of existing methodological approaches to the collection, compilation and processing of large data sets, including statistical, neural network and fuzzy methods, a method for combined analysis and modeling of energy consumption processes in buildings has been developed. At the same time, a methodology was proposed for the selection of a set of measures to optimize the energy efficiency of buildings. The original model and method of choosing energy saving measures, taking into account a flexible multi-level structure, as well as various compatibility and significance of the indicators being evaluated, are proposed. The proposed visualization algorithm allows to combine on one graph economic criteria of energy-saving projects payback and energy-saving effect, which is a reflection of natural indicators of energy efficiency. This model allows to formalize the process of increasing the buildings energy efficiency, and can be useful for energy managers, who are responsible for energy flows optimization.