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Modelling Energy Data in a Generalized Additive Model—A Case Study of Colombia

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Listed:
  • Lina Berbesi

    (Department of Statistics, Faculty of Science, University of Auckland, Auckland 1010, New Zealand)

  • Geoffrey Pritchard

    (Department of Statistics, Faculty of Science, University of Auckland, Auckland 1010, New Zealand)

Abstract

Energy demand modelling is essential for reliable informing and framing energy policy decisions. More accurate modelling betters ensuring availability of energy and energy quality. Energy availability is related to energy access across the country and defines important economic measures such as energy poverty, which plays a critical role in developing countries. Energy quality is related to the reliability of the supply for correctly estimating energy needs. To incorporate spatial and temporal components of energy in a way that availability and quality are accurately assessed, this article discussed a number of suitable task methods for this (Second-generation GAMs with one-dimensional smoothers: Cyclic/Non-Cyclic Cubic Splines and two-dimensional smoothers: Markov Random Fields/Tensor Splines Interactions). The results showed that the complete consideration of both temporal and spatial aspects leads to a better fitted model which explains more of the data variation.

Suggested Citation

  • Lina Berbesi & Geoffrey Pritchard, 2023. "Modelling Energy Data in a Generalized Additive Model—A Case Study of Colombia," Energies, MDPI, vol. 16(4), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1929-:d:1069198
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    References listed on IDEAS

    as
    1. Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian inference for generalized additive mixed models based on Markov random field priors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 201-220.
    2. Fahrmeir, Ludwig & Kneib, Thomas, 2011. "Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data," OUP Catalogue, Oxford University Press, number 9780199533022.
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