Planning the Future Electricity Mix for Countries in the Global South: Renewable Energy Potentials and Designing the Use of Artificial Neural Networks to Investigate Their Use Cases

Abstract

Due to a symbiotic relationship, economic growth leads to greater energy consumption in transportation, manufacturing, and domestic sectors. Electricity consumption in the global south is rising as nations in the region strive for economic development. Due to the high costs of fossil fuels and environmental issues, these countries are planning exploitation of their renewable energy potential for meeting their energy needs. In this paper, we take Myanmar as a case study for which photovoltaic (PV) is seen as the preferred technology owing to its modular nature and Myanmar’s tremendous PV potential. To create sustainable systems, the impact of diurnal PV profiles on electricity demand profiles needs investigating. Accurate load forecasts lead to significant savings in operation and planning and maintenance. Artificial neural networks (ANNs) can easily be used for load profile forecasting. This work proposes a three-stage systematic approach which could be employed by global south countries for designing ANN load forecasting models with the aim of simplifying the design process. While the results of this work demonstrate that PV is a suitable energy source for countries like Myanmar, they also point to the importance of including annual load increase rate and PV output degradation rate in system planning.