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

Hye-Yeong Lee, Kee Moon Jang and Youngchul Kim
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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

Energies, 2020, vol. 13, issue 17, 1-17

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.

Keywords: artificial neural network; energy consumption; energy demand; urban growth; night-time satellite light data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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