Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

Seunghui Lee,Jaewook Lee,Sungwon Jung

doi:10.3390/en12040608

Abstract

The evaluation of building energy consumption is heavily based on building characteristics and thus often deviates from the true consumption. Consequently, user-based estimation of building energy consumption is necessary because the actual consumption is greatly affected by user characteristics and activities. This work aims to examine the variation in energy consumption as a function of user activities within the same building, and to employ an artificial neural network (ANN) to predict user-based energy consumption. The study exploited the actual 24-h schedules of 5240 single-person households and computed the respective energy consumption using EnergyPlus V 8.8.0 software. The calculated values were clustered according to gender, age, occupation, income, educational level, and occupancy period and the difference among them was analyzed. The simulation results showed that for single-person households in Korea, females used more energy than males did, and the difference increased with age. Furthermore, unemployed and low-income individuals consumed more energy whereas consumption was inversely proportional to the educational level. Energy consumption increased with the occupancy period. Based on the simulation results and six user characteristics, the ANN model indicated a correlation between user characteristics and energy usage. This study analyzed the differences in energy usage depending on user activity and characteristics that affect building energy consumption.

Highlights

Buildings are responsible for a high percentage of CO2 emissions in cities as well as 40% of the total energy consumption worldwide [1]
In Korea, previous work on energy prediction using an artificial neural network (ANN) relied on building characteristics or environmental conditions whereas the present work was based on different user activities in a particular building as well as demographic, social, and economic characteristics
We tried to improve the accuracy by utilizing actual user activity and characteristic data and we drew

Summary

Introduction

Buildings are responsible for a high percentage of CO2 emissions in cities as well as 40% of the total energy consumption worldwide [1]. Building energy performance assessments are conducted according to national standards and performance ratings must be disclosed during real estate transactions. In Europe, for instance, all countries registered in Energy Performance of Building. Directives (EPBD) since 2009 are required to disclose the building energy performance rating to the market. A similar policy has been adopted by the city of Seoul, Korea since 2013 and is gradually expanding to other cities to raise awareness and encourage the development of energy-efficient buildings, green remodeling revitalization. Majcen et al observed that the total amount of energy used by the occupants of buildings with a high energy performance rating was higher than the estimated value [2]. Researchers refer to Energies 2019, 12, 608; doi:10.3390/en12040608 www.mdpi.com/journal/energies

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