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

Author

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  • Seunghui Lee

    (Department of Architecture, Sejong University, Seoul 05006, Korea)

  • Sungwon Jung

    (Department of Architecture, Sejong University, Seoul 05006, Korea)

  • Jaewook Lee

    (Department of Architectural Engineering, Sejong University, Seoul 05006, Korea)

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.

Suggested Citation

  • Seunghui Lee & Sungwon Jung & Jaewook Lee, 2019. "Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea," Energies, MDPI, vol. 12(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:608-:d:206072
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    References listed on IDEAS

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    9. Mansu Kim & Sungwon Jung & Joo-won Kang, 2019. "Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea," Sustainability, MDPI, vol. 12(1), pages 1-28, December.
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