Deep neural network approach for prediction of heating energy consumption in old houses

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

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Article number122
JournalEnergies
Volume14
Issue number1
DOIs
Publication statusPublished - 2021 Jan 1

Bibliographical note

Funding Information:
Acknowledgments: This work was conducted under the framework of Research and Development Program of the Korea Institute of Energy Research (C0-2411), and was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019M3E7A111308912).

Publisher Copyright:
© 2020 by the authors. Li-censee MDPI, Basel, Switzerland. This.

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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