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Modeling electricity consumption using nighttime light images and artificial neural networks

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  • Jasiński, Tomasz

Abstract

The purpose of this paper is to model electricity consumption using Artificial Neural Networks (ANN). Total electricity consumption and consumption generated by households (HH) were modeled. The input variables of the ANN were based on nighttime light images from VIIRS DNB. Studies conducted thus far have covered mainly linear models. Most of case studies focused on single countries or groups of countries with only few focusing on the sub-national scale. This paper is pioneering in covering an area of Poland (Central Europe) at NUTS-2 level. The use of ANN enabled the modeling of the non-linear relations associated with the complex structure of electricity demand. Satellite data were collected for the period 2013–2016, and included images with improved quality (inter alia higher resolution), compared to the DMSP/OLS program. As images are available from April 2012 onwards, it is only recently that their number has become sufficient for ANN learning. The images were used to create models of multilayer perceptrons. The results achieved by ANN were compared with the results obtained using linear regressions. Studies have confirmed that electricity consumption can be determined with higher precision by the ANN method.

Suggested Citation

  • Jasiński, Tomasz, 2019. "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, vol. 179(C), pages 831-842.
    • Handle: RePEc:eee:energy:v:179:y:2019:i:c:p:831-842
    DOI: 10.1016/j.energy.2019.04.221
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    Cited by:

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    2. Piotr Wójcik & Krystian Andruszek, 2022. "Predicting intra‐urban well‐being from space with nonlinear machine learning," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(4), pages 891-913, August.
    3. Naeher,Dominik & Narayanan,Raghavan & Ziulu,Virginia, 2021. "Impacts of Energy Efficiency Projects in Developing Countries : Evidence from a SpatialDifference-in-Differences Analysis in Malawi," Policy Research Working Paper Series 9842, The World Bank.
    4. Du, Mengbing & Ruan, Jianhui & Zhang, Li & Niu, Muchuan & Zhang, Zhe & Xia, Lang & Qian, Shuangyue & Chen, Chuchu, 2024. "China's local-level monthly residential electricity power consumption monitoring," Applied Energy, Elsevier, vol. 359(C).
    5. Hu, Ting & Wang, Ting & Yan, Qingyun & Chen, Tiexi & Jin, Shuanggen & Hu, Jun, 2022. "Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS," Applied Energy, Elsevier, vol. 322(C).
    6. Zhong, Liang & Liu, Xiaosheng & Ao, Jianfeng, 2022. "Spatiotemporal dynamics evaluation of pixel-level gross domestic product, electric power consumption, and carbon emissions in countries along the belt and road," Energy, Elsevier, vol. 239(PA).
    7. Kwak, Sanghyeok & Choi, Jaehong & Lee, Min Chul & Yoon, Youngbin, 2021. "Predicting instability frequency and amplitude using artificial neural network in a partially premixed combustor," Energy, Elsevier, vol. 230(C).

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    More about this item

    Keywords

    Electricity consumption; Nighttime light images; Artificial neural networks;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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