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Electricity Consumption Forecasting Using Time Series Analysis

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Advances in Computing and Data Sciences (ICACDS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 906))

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Abstract

The demand for electricity has been continuously increasing over the years. To understand the future consumption, a good predictive model is entailed. The ARIMA models have been extensively used for time series prediction showing encouraging results. In this paper, an attempt is made on forecasting the electricity consumption using the ARIMA model. Using the mean absolute percentage error (MAPE) to measure forecast accuracy, the model was able to forecast with an error of 6.63%. Results shows that the ARIMA model has a potential to compete with existing techniques for electricity consumption forecast.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, Jharkhand, India

    Praphula Kumar Jain, Waris Quamer & Rajendra Pamula

Authors
  1. Praphula Kumar Jain
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  2. Waris Quamer
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  3. Rajendra Pamula
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Corresponding author

Correspondence to Praphula Kumar Jain .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Information Technology, Solan, India

    P. K. Gupta

  3. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. Institute of Information Theory and Automation, Prague 8, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, Canada

    Tuncer Ören

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Jain, P.K., Quamer, W., Pamula, R. (2018). Electricity Consumption Forecasting Using Time Series Analysis. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_33

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  • DOI: https://doi.org/10.1007/978-981-13-1813-9_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1812-2

  • Online ISBN: 978-981-13-1813-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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