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Energy Consumption Prediction in Vietnam with an Artificial Neural Network-Based Urban Growth Model

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  • Hye-Yeong Lee

    (KAIST Urban Design Lab, Department of Civil and Environmental Engineering, KAIST, Daejeon 34141, Korea)

  • Kee Moon Jang

    (KAIST Urban Design Lab, Department of Civil and Environmental Engineering, KAIST, Daejeon 34141, Korea)

  • Youngchul Kim

    (KAIST Urban Design Lab, Department of Civil and Environmental Engineering, KAIST, Daejeon 34141, Korea)

Abstract

In developing countries, energy planning is important in the development planning due to high rates of economic growth and energy demand. However, existing approaches of energy prediction, using gross domestic product, hardly demonstrate how much energy specific regions or cities may need in the future. Thus, this study seeks to predict the amount of energy demand by considering urban growth as a crucial factor for investigating where and how much energy is needed. An artificial neural network is used to forecast energy patterns in Vietnam, which is a quickly developing country and seeks to have an adequate energy supply. Urban growth factors, population, and night-time light intensity are collected as an indicator of energy use. The proposed urban-growth model is trained with data of the years 1995, 2000, 2005, and 2010, and predicts the light distribution in 2015. We validated the model by comparing the predicted result with actual light data to display the spatial characteristics of energy-consumption patterns in Vietnam. In particular, the model with urban growth factors estimated energy consumption more closely to the actual consumption. This spatial prediction in Vietnam is expected to help plan geo-locational energy demands.

Suggested Citation

  • Hye-Yeong Lee & Kee Moon Jang & Youngchul Kim, 2020. "Energy Consumption Prediction in Vietnam with an Artificial Neural Network-Based Urban Growth Model," Energies, MDPI, vol. 13(17), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4282-:d:400878
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    References listed on IDEAS

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    Cited by:

    1. Karodine Chreng & Han Soo Lee & Soklin Tuy, 2022. "A Hybrid Model for Electricity Demand Forecast Using Improved Ensemble Empirical Mode Decomposition and Recurrent Neural Networks with ERA5 Climate Variables," Energies, MDPI, vol. 15(19), pages 1-26, October.
    2. Wang, Meng & Wang, Wei & Wu, Lifeng, 2022. "Application of a new grey multivariate forecasting model in the forecasting of energy consumption in 7 regions of China," Energy, Elsevier, vol. 243(C).
    3. Batara Surya & Andi Muhibuddin & Seri Suriani & Emil Salim Rasyidi & Baharuddin Baharuddin & Andi Tenri Fitriyah & Herminawaty Abubakar, 2021. "Economic Evaluation, Use of Renewable Energy, and Sustainable Urban Development Mamminasata Metropolitan, Indonesia," Sustainability, MDPI, vol. 13(3), pages 1-45, January.
    4. Zhiyuan Wang & Felix Bachofer & Jonas Koehler & Juliane Huth & Thorsten Hoeser & Mattia Marconcini & Thomas Esch & Claudia Kuenzer, 2022. "Spatial Modelling and Prediction with the Spatio-Temporal Matrix: A Study on Predicting Future Settlement Growth," Land, MDPI, vol. 11(8), pages 1-23, July.

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