An advanced data analysis method using heating degree-days for estimating the efficiency of buildings in smart cities: A case study of Astana

Heating of buildings account for over 40% of global energy consumption and approximately 30% of carbon dioxide emissions. In Kazakhstan, achieving optimal energy efficiency and thermal comfort in residential buildings is particularly challenging due to region's unique climatic conditions and a lack of detailed microclimate data. Study addresses these challenges by aiming to identify effective combinations of microclimate and personal thermoregulation parameters that would allow a one-degree reduction in heating temperature without sacrificing comfort. In paper novel method for assessing building energy efficiency using energy consumption bills and both internal and external air temperature data has been developed. This method utilizes Heating Degree-Days (HDD), which calculates daily temperature variances rather than monthly averages, providing a more accurate assessment of energy needs. Analysis of heating data from 355 houses indicated that buildings with poor insulation show an exponential increasing rate of heat losses on colder and windier days. In contrast, well-insulated buildings maintain a linear relationship between HDD and energy consumption. It was evident when normal temperature was decreased by 1°C the number of efficient buildings were increased from 273 to 284, while the number of inefficient and normal houses were decreased from 56 to 53 and from 26 to 18, respectively. Аindings enables municipalities to use heating bill data and daily weather data to remotely identify poorly insulated houses, necessitating physical inspections by energy auditors to confirm inefficiencies and implement necessary improvements. Moreover, a reduction of ΔT=1°C allowed to save around ∼$95,000 for residents of 355 houses in heating season.