A bottom-up bayesian extension for long term electricity consumption forecasting

• Published in: Energy (Oxford) Vol. 167; pp. 198 - 210
• Main Authors: da Silva, Felipe L.C., Cyrino Oliveira, Fernando L., Souza, Reinaldo C.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15-01-2019; Elsevier BV
• Subjects: Bayesian analysis; Bayesian inference; bottom-up approach; Decision making; Electricity; Electricity consumption; Energy efficiency; Forecasting; Hierarchical linear model; Long term; Long term forecasting; Markov chain Monte Carlo methods; Mathematical models; Parameter estimation; Probability distribution; Pulp; Pulp & paper industry; Statistical inference; Hierarchical linear model; Long term forecasting; Markov chain Monte Carlo methods; Bayesian inference; bottom-up approach
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2018.10.201

Long term electricity consumption forecasting has been extensively investigated in recent years in different countries due to its economic and social importance. In this context, the long term electricity consumption projections of a country or region are highly relevant for decision-making of companies and organizations operating in any energy system. In this paper, it is proposed a methodology that combines the bottom-up approach with hierarchical linear models for long term electricity consumption forecasting of a particular industrial sector considering energy efficiency scenarios. In addition, the Bayesian inference is used for model parameter estimation and, enabling the inclusion of uncertainty in the forecasts produced by the model. The model was applied to the Brazilian pulp and paper industry and it was able to capture the trajectory of the real consumption observed during the 2008–2014 period. The model was also used to generate long term point and probability distribution forecasts for the period ranging from 2015 until 2050.
•Bottom-up Bayesian extension for the long-term electricity consumption forecasting.•The proposed model combines the bottom-up approach with hierarchical linear models.•The proposed model considers energy efficiency scenarios.