A hybrid stochastic model based Bayesian approach for long term energy demand managements

In this study, a hybrid stochastic model (BScA model) using Bayesian approach and scenario analysis to forecast long term energy demand is developed. The main objective of this study is to design and develop a model for energy analysis in demand side and describe the energy saving and GHG reduction potential on the other. For this, total energy demand is selected as the response variable and primary energy production, population, GDP and natural gas and gasoline prices are chosen as covariates. Also, Political drivers, economic drivers, social-environmental and technological drivers are the key driving forces for scenario development. After interview and ranking the drivers, we have built scenario matrixes and reducing them upon strengths, weaknesses, opportunities and how the energy system perspective in each of the scenarios develop. Results show that primary energy production and population growth have positive impact on energy demand. And Energy consumption would decrease with energy price increase. And, economic development would rise energy demand. Also, the total potential for energy saving is equal to 3663 MBOE in duration of 2016–2040. Results demonstrate the energy intensity (EI) will be 2.12 MBOE/Million Rials in 2040 if energy saving solutions are taken. And, the carbon emission will reduce about 32% in 2040. Keywords: Bayesian, Energy demand, Scenario analysis, Hybrid stochastic model, Energy intensity, Energy saving