Application of Machine Learning Methods for the Analysis of Heat Energy Consumption by Zones with a Change in Outdoor Temperature: Case Study for Nur-Sultan City

Abstract

The environmental situation in the capital city is always in the focus of attention of the municipal authorities of the city and is one of the most important factors influencing the decisions. The capital of Kazakhstan, Nur-Sultan, consumes heat energy generated from fossil fuel, and one of the major problems is an extremely cold and long winter. The GHG emissions and particle matters from the coal-based Combined Heat and Power plant have a significant impact on the environment as smog, particularly in the heating season. This work analyses spatial high-resolution Big Data collected from the metering points of 385 houses and 62 heat transmission contours across a city during the heating season. The temporary resolution was 10 minutes i.e., 8754 rows for 5 months (Jan-May). There are shown the findings of the correlation rates analysis between heat energy consumption by zones of Nur-Sultan and ambient temperature, as well as non-efficient zones with substantial losses. In this paper for developing the prediction tools for the Smart City heat consumption there were used mixed modelling methods and machine learning approaches, such as Linear Regression, K-neighbours Regressor, and Random Forest Regressor models. These obtained results could be helpful for predicting optimal pumping pressure for each distribution network using machine learning technologies and finding overheated contours in real time.