Comparative analysis of power consumption time series in deprived and developed regions of Iran
Corresponding Author(s) : Masoud Safarishaal
Future Energy,
Vol. 3 No. 1 (2024): February 2024 Issue
Abstract
This paper presents a comparative analysis of power consumption time series at 12 o'clock every day between 2020 and 2022 for one distribution network in Sistan and one in Tehran. The aim of this study is to compare the development and climate differences between these regions, as well as the impact of social, industrial, and environmental factors. By comparing a deprived area with an area in the capital, we aim to identify potential disparities in power consumption and identify potential areas for improvement. We employed the CRP tool software and toolkit for time series analysis and used various methods to compare and predict the predictability of each time series. Our findings suggest significant differences in power consumption between the two regions, which could be attributed to socio-economic and environmental factors. Overall, this study sheds light on the potential impact of regional differences on power consumption and highlights the need for further research in this area.
Keywords
Download Citation
Endnote/Zotero/Mendeley (RIS)BibTeX
- Bunde, A. & Piersol, A.G. (2003). Random Data: Analysis and Measurement Procedures (3rd ed.). John Wiley & Sons. DOI:10.1002/9781118032428
- Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice (2nd ed.). OTexts. ISBN: 978-0987507112
- Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting. Springer.
- Galit Shmueli and Kenneth C. Lichtendahl Jr “Practical Time Series Forecasting with R: A Hands-On Guide [2nd Edition] (Practical Analytics)” Jul 19, 2016
- Rami Krispin “Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R” May 31, 2019
- Shumway, R. H., & Stoffer, D. S. (2017). Time series analysis and its applications: with R examples (4th ed.). Springer.
- Hossain, M. S., Pota, H. R., & Ali, M. A. (2019). "A review of electricity load forecasting techniques." Renewable and Sustainable Energy Reviews, 103, 29-43.
- Raza, S. A., Siddiqui, S. A., & Ahmed, S. (2020). "Forecasting daily peak electricity demand using artificial neural networks: A case study of Pakistan." Energy Reports, 6, 346-352.
- Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). "Forecasting with artificial neural networks: the state of the art." International journal of forecasting, 14(1), 35-62.
- Hong, T., & Fan, S. (2016). "Short-term load forecasting using a hybrid model of wavelet transform, ARIMA and support vector regression." Applied Energy, 178, 188-198.
References
Bunde, A. & Piersol, A.G. (2003). Random Data: Analysis and Measurement Procedures (3rd ed.). John Wiley & Sons. DOI:10.1002/9781118032428
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice (2nd ed.). OTexts. ISBN: 978-0987507112
Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting. Springer.
Galit Shmueli and Kenneth C. Lichtendahl Jr “Practical Time Series Forecasting with R: A Hands-On Guide [2nd Edition] (Practical Analytics)” Jul 19, 2016
Rami Krispin “Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R” May 31, 2019
Shumway, R. H., & Stoffer, D. S. (2017). Time series analysis and its applications: with R examples (4th ed.). Springer.
Hossain, M. S., Pota, H. R., & Ali, M. A. (2019). "A review of electricity load forecasting techniques." Renewable and Sustainable Energy Reviews, 103, 29-43.
Raza, S. A., Siddiqui, S. A., & Ahmed, S. (2020). "Forecasting daily peak electricity demand using artificial neural networks: A case study of Pakistan." Energy Reports, 6, 346-352.
Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). "Forecasting with artificial neural networks: the state of the art." International journal of forecasting, 14(1), 35-62.
Hong, T., & Fan, S. (2016). "Short-term load forecasting using a hybrid model of wavelet transform, ARIMA and support vector regression." Applied Energy, 178, 188-198.