Modeling and prediction of primary energy supply and electricity generation structures based on Markov chain: an insight with focus on the role of natural gas in Pakistan

摘要

This study aims to develop an alternative trended matrices approach of structural prediction as a counterpart to the classical Chapman-Kolmogorov approach. Methodological addition lies within the way of preservation of stochasticity of the transition matrices originated from the alternative approach for the case of initial non-stochasticity due to all negative row elements. Ten datasets (various case studies) of historic energy structures have been modeled, simulated, and extended as Markov chains followed by their respective error assessments. Linear regression-based alternative approach is found to be the nearest counterpart to the classical approach for the majority of datasets. This approach is subsequently applied for the prediction of primary energy supply and electricity generation structures in Pakistan in business as usual scenario for 2018-2030 period. Fossil fuels will share about 90% in the primary energy supply and electricity generation. Moreover, gas will prevail as the major fossil fuel for both primary energy supply and thermal-dominant electricity generation mixes. Gas share will be about 70% within the thermal electricity generation mix. Some results depict the hurdles in the development of a low carbon future. The alternative prediction approach may be used for comparable fields of resource allocation, planning, and management.