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Pseudo-Inverse Matrix Model for Estimating Long-Term Annual Peak Electricity Demand: The Covenant University s Experience

Author

Listed:
  • Ademola Abdulkareem

    (Department of Electrical and Information Engineering, Covenant University, Ota, Ogun State, Nigeria.)

  • E. J. Okoroafor

    (Department of Electrical and Information Engineering, Covenant University, Ota, Ogun State, Nigeria.)

  • Ayokunle Awelewa

    (Department of Electrical and Information Engineering, Covenant University, Ota, Ogun State, Nigeria.)

  • Aderibigbe Adekitan

    (Department of Electrical and Information Engineering, Covenant University, Ota, Ogun State, Nigeria.)

Abstract

One of the major decision problems facing any electrical supply undertaking is the forecasting of peak power demand. A problem therefore arises when an estimate of future electricity demand is not known to prepare for impending possible increase in electricity demand. To overcome this problem, it is therefore imperative to evaluate the precise amount of energy required for a sustainable power supply to customers. In line with this goal, this study established a mathematical model of regression analysis using Pseudo-Inverse Matrix (PIM) method for the assessment of the historical data of Covenant University s electric energy consumption. This method predicts a more accurate and reliable future energy requirement for the community, with special consideration for the next one decade. The accuracy of prediction based on the use of PIM method is compared with the forecast result of the Least Squares Model (LSM), commonly used by engineers in making long-term forecast. The error analysis result from the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) performed on the two models using Mean Absolute Deviation (MAD) shows that the PIM is the most accurate of the models. Though this method is examined using a University community, it can be further extended to cover the whole country, provided the historical data of the country s past electric energy consumptions is available.

Suggested Citation

  • Ademola Abdulkareem & E. J. Okoroafor & Ayokunle Awelewa & Aderibigbe Adekitan, 2019. "Pseudo-Inverse Matrix Model for Estimating Long-Term Annual Peak Electricity Demand: The Covenant University s Experience," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 103-109.
    • Handle: RePEc:eco:journ2:2019-04-13
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    References listed on IDEAS

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    1. repec:eco:journ2:2017-04-33 is not listed on IDEAS
    2. Poonpong Suksawang & Sukonthip Suphachan & Kanokkarn Kaewnuch, 2018. "Electricity Consumption Forecasting in Thailand using Hybrid Model SARIMA and Gaussian Process with Combine Kernel Function Technique," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 98-109.
    3. Irina A. Firsova & Dinara G. Vasbieva & Nikolay N. Kosarenko & Maria A. Khvatova & Lev R. Klebanov, 2019. "Energy Consumption Forecasting for Power Supply Companies," International Journal of Energy Economics and Policy, Econjournals, vol. 9(1), pages 1-6.
    4. V. Ramesh Kumar & Pradipkumar Dixit, 2018. "Artificial Neural Network Model for Hourly Peak Load Forecast," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 155-160.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    s error analysis; historical data; linear regression; peak demand; pseudo-inverse matrix.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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