Deep Neural Network Approach for Prediction of Heating Energy Consumption in Old Houses

Sungjin Lee,Soo Cho,Taeyeon Kim,Seo-Hoon Kim,Jonghun Kim,Hakgeun Jeong,Suyong Chae

doi:10.3390/en14010122

Sungjin Lee, Soo Cho + Show 5 more

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https://doi.org/10.3390/en14010122

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Journal: Energies	Publication Date: Dec 28, 2020
Citations: 15	License type: CC BY 4.0

Affiliation: Korea Institute of Energy Research, Yonsei University

Abstract

Neural network models are data-driven and are effective for predicting and interpreting nonlinear or unexplainable physical phenomena. This study collected building information and heating energy consumption data from 16,158 old houses, selected key input variables that affect the heating energy consumption based on the collected datasets, and developed a deep neural network (DNN) model that showed the highest accuracy for the prediction of heating energy consumption in an old house. As a result, 11 key input variables were selected, and an optimal DNN model was developed. This optimal DNN model showed the highest prediction accuracy (R2 = 0.961) when the number of hidden layers was five and the number of neurons was 22. When the optimal DNN model was applied for the standard model of low-income detached houses, the prediction accuracy (Cv(RMSE)) of the optimal DNN model, compared to the EnergyPlus calculation result, was 8.74%, which satisfied the ASHRAE standard sufficiently.

Highlights

According to the 2018 National Housing Information Survey of the Korean StatisticsInformation Service (KOSIS), there are about 17.63 million houses in Korea, 9% of which were built before 1979 when housing insulation standards were enacted
Old houses are vulnerable to a lack of insulation and poor airtightness, which may cause heat loss and excessive energy consumption
This study aimed to develop a deep neural network (DNN) model for predicting the heating energy consumption for 16,158 old houses in Korea

Summary

Introduction

Information Service (KOSIS), there are about 17.63 million houses in Korea, 9% of which were built before 1979 when housing insulation standards were enacted. Of those buildings, 47% are more than 20 years old [1]. Old houses are vulnerable to a lack of insulation and poor airtightness, which may cause heat loss and excessive energy consumption. For this reason, the government announced the 3rd National Energy Plan and has made efforts to improve the energy welfare system and to reduce the energy consumption of old houses. Due to lack of diagnostic equipment, manpower and the long diagnostic time required, it is difficult to measure all parameters affecting energy consumption and to predict energy consumption by the parameters

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Conclusion