Heating Performance Analysis for Short-Term Energy Monitoring and Prediction Using Multi-Family Residential Energy Consumption Data

Sukjoon Oh,Kee Han Kim,Chul Kim,Sung Lok Do,Joonghyeok Heo

doi:10.3390/en13123189

Abstract

Many smart apartments and renovated residential buildings have installed Smart Meters (SMs), which collect interval data to accelerate more efficient energy management in multi-family residential buildings. SMs are widely used for electricity, but many utility companies have been working on systems for natural gas and water monitoring to be included in SMs. In this study, we analyze heating energy use data obtained from SMs for short-term monitoring and annual predictions using change-point models for the coefficient checking method. It was found that 9-month periods were required to search the best short-term heating energy monitoring periods when non-weather-related and weather-related heating loads and heating change-point temperatures are considered. In addition, the 9-month to 11-month periods were needed for the analysis to apply to other case study residences in the same high-rise apartment. For the accurate annual heating prediction, 11-month periods were necessary. Finally, the results from the heating performance analysis of this study were compared with the cooling performance analysis from a previous study. This study found that the coefficient checking method is a simple and easy-to-interpret approach to analyze interval heating energy use in multi-family residential buildings. It was also found that the period of short-term energy monitoring should be carefully selected to effectively collect targeted heating and cooling data for an energy audit or annual prediction.

Highlights

In a modern society, residential buildings are the primary source of national energy consumption, which use energy for occupants’ comfort and building system operations
The shortened monitoring periods were useful to save time and costs to measure non-weather-related heating load and weather-related heating load
The predicted annual heating energy use from the change-point model was compared to the measured annual usage to verify the accuracy of the coefficient checking method for residential heating loads

Summary

Introduction

Residential buildings are the primary source of national energy consumption, which use energy for occupants’ comfort and building system operations. South Korea has developed an ambitious plan to improve building energy efficiency protocols. Zero-energy buildings will be mandatory for all types of new constructions after 2030 [1]. Smart apartments and renovated residential buildings have installed Smart Meters (SMs), called Advanced Metering Infrastructure (AMI), which collect interval energy use data to accelerate more efficient energy management in multi-family residential buildings. AMI has great potential for energy use analysis applications by collecting and monitoring energy use data from buildings. The system combines software and hardware components, data management, and monitoring systems using a two-way real-time communication network [2,3].

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Conclusion