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

Lina Berbesi and Geoffrey Pritchard ()
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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

Energies, 2023, vol. 16, issue 4, 1-20

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.

Keywords: generalized additive models; cubic splines; Markov random fields; tensor product splines; energy modelling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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