Machine Learning Methods to Increase the Energy Efficiency of Buildings

Abstract

Predicting a building’s energy consumption plays an important role as it can help assess its energy efficiency, identify and diagnose energy system faults, and reduce costs and improve climate impact. An analysis of current research in the field of ensuring the energy efficiency of buildings, in particular, their energy assessment, considering the types of models under consideration, was carried out. The principles, advantages, limitations, and practical application of the main data-based models are considered in detail, and priority future directions for forecasting the energy efficiency of buildings are highlighted. It is shown that the effectiveness of the methods is different for the main types of models and depends on the following factors: input data and parameters, the type and quality of available data for training, the suitability of the method for a specific type of model, etc. The need to consider the element of uncertainty when forecasting energy consumption due to the impossibility of accurate modeling of meteorological factors and the behavior of residents is emphasized. Therefore, machine learning methods, particularly deep learning-based models, are chosen to represent complex nonlinear input-output relationships, as they show higher performance than statistical time series forecasting methods. The analysis of published works revealed a lack of works describing a comprehensive energy forecasting information system for use in commercial projects. We proposed a new approach to combining semantic modeling and machine learning technologies for the energy management system of smart buildings, using the knowledge system of the semantic model we developed.