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Bayesian and network models with covariate effects for predicting heating energy demand.

Pablo Juan1, Marta Braulio-Gonzalo2, Carlos Díaz-Ávalos3

  • 1IMAC, Department of Mathematics, Universitat Jaume I, Castellón, Spain; Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain.

Spatial and Spatio-Temporal Epidemiology
|December 2, 2022
PubMed
Summary

Spatial location significantly impacts residential heating energy demand. This study introduces advanced Bayesian methods, including Stochastic Partial Differential Equations and simulated street networks, to accurately model this demand.

Keywords:
CovariatesHeating energy demandINLANetworksSPDE

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Area of Science:

  • Geostatistics
  • Spatial analysis
  • Energy demand modeling

Background:

  • Spatial effects are crucial in geostatistical studies of residential heating energy demand.
  • Existing methods often rely on descriptive statistics or Markov random fields.
  • Incorporating spatial data enhances the accuracy of energy demand predictions.

Purpose of the Study:

  • To propose and evaluate two novel Bayesian methodologies for modeling variable heating energy demand.
  • To investigate the influence of spatial location and covariates on heating energy consumption.
  • To demonstrate the utility of simulated street networks in spatial data analysis.

Main Methods:

  • Stochastic Partial Differential Equations (SPDE) with Integrated Nested Laplace Approximation (INLA) for spatial modeling.
  • Bayesian methodology applied to incorporate covariates and spatial effects.
  • Generation and analysis of simulated street networks for data modeling.

Main Results:

  • Building location is a critical factor in determining heating energy demand.
  • The proposed Bayesian methods effectively model spatial variations in energy consumption.
  • Covariates and spatial effects significantly improve the accuracy of heating demand predictions.

Conclusions:

  • Spatial analysis is essential for accurate residential heating energy demand modeling.
  • Advanced Bayesian techniques offer robust solutions for complex spatial data.
  • Future studies should leverage spatial information for more precise energy planning.