Analyzing Hourly Energy Consumption Profiles of Commercial Buildings Through Statistical Modeling

High-resolution energy data are critical for optimizing energy systems in commercial buildings and integrating renewable energy sources. While detailed hourly energy consumption data are essential for aligning energy production with demand, most available data, such as monthly energy bills, lack the necessary granularity. Understanding customer consumption is crucial for assessing expected loads at strategic nodes and informing power system planning strategies. This study introduces an innovative mathematical method to derive hourly energy consumption profiles from readily available data sources. Unlike traditional methods requiring large datasets and statistical techniques, our approach is designed for simplicity and accessibility, particularly for the commercial sector. The methodology uses a mathematical reconstruction technique to transform aggregated data, such as monthly energy bills, into detailed hourly profiles. These profiles are based on a billing structure divided into three tariffs: weekdays, Saturdays, and Sundays. Key results show that energy consumption is highest during operational hours and lowest during non-operational hours, providing insight into load variation patterns. The method generated hourly load profiles over a year using actual load data from the distribution system operators (DSOs), effectively cap-turing energy consumption patterns during operational and non-operational hours across various periods and conditions. This approach enhances energy planning and management, supporting the integration of renewable energy into the commercial sector's energy mix.