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

Nafeesa Javed, Muhammad Javaid Iqbal, Sohail Masood, Laiba Rehman and Saba Ramzan
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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

Bulletin of Business and Economics (BBE), 2024, vol. 13, issue 1, 777-783

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.

Keywords: Energy Consumption; Smart Meter; Optimization; Machine Learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:rfh:bbejor:v:13:y:2024:i:1:p:777-783

DOI: 10.61506/01.00269

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