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The Effect of Climate Change on Energy Consumption Using Smart Meter Dataset

Author

Listed:
  • Nafeesa Javed

    (Faculty of Computer Science and Information Technology, Superior University, Lahore 54000, Pakistan)

  • Muhammad Javaid Iqbal

    (Faculty of Computer Science and Information Technology, Superior University, Lahore 54000, Pakistan, Intelligent Data Visual Computing Research (IDVCR), Lahore 55150, Pakistan)

  • Sohail Masood

    (Faculty of Computer Science and Information Technology, Superior University, Lahore 54000, Pakistan, Intelligent Data Visual Computing Research (IDVCR), Lahore 55150, Pakistan)

  • Laiba Rehman

    (Faculty of Computer Science and Information Technology, Superior University, Lahore 54000, Pakistan)

  • Saba Ramzan

    (Faculty of Computer Science and Information Technology, Superior University, Lahore 54000, Pakistan)

Abstract

Electricity use in the urban areas is more than in the rural areas because the ratio of the population is higher in the urban areas as compared to rural areas. Energy consumption increasing day by day worldwide, so there is a need to give the best plan for the best energy resource consumption to the producers. On the other side, various other energy types are also becoming most useable in the world due to many factors like an increase in usage due to population, line losses, loss of energy due to low-quality material, and especially usage ratio increases as compared to the production of energy. This helps to save energy from misuse and to utilize the energy properly. There are various approaches applied to forecast energy consumption but, in this study, we proposed the system using LSTM, ARIMA, and Prophet model to give the solution for smart meter dataset energy consumption forecasting in a good way. After applying this approach, we conclude that the weather variables are the major factors in energy consumption such that the temperature effect is larger than other variables. The proposed system proves its performance by forecasting the dataset using these algorithms and calculate the high-grade visual graphs.

Suggested Citation

  • Nafeesa Javed & Muhammad Javaid Iqbal & Sohail Masood & Laiba Rehman & Saba Ramzan, 2024. "The Effect of Climate Change on Energy Consumption Using Smart Meter Dataset," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(1), pages 777-783.
    • Handle: RePEc:rfh:bbejor:v:13:y:2024:i:1:p:777-783
    DOI: https://doi.org/10.61506/01.00269
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    References listed on IDEAS

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    1. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    2. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    3. Lange, Steffen & Pohl, Johanna & Santarius, Tilman, 2020. "Digitalization and energy consumption. Does ICT reduce energy demand?," Ecological Economics, Elsevier, vol. 176(C).
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