Smart metering and energy access programs : an approach to energy poverty reduction in Sub-Saharan Africa

Abstract

Evolving technologies can provide continuous and accurate energy data to plan, implement, and maintain energy systems for areas where electricity access is a challenge, particularly in sub-Saharan Africa (SSA) where over 53% of the world’s energy-poor population resides. This research aims to analyse the applicability of smart metering data to the sustainable energy access planning (SEAP) framework for energy access programs (EAPs), toward the reduction of energy poverty in SSA. Household energy data based on energy access criteria from an SSA country was generated using smart metering technologies, then applied to the analysis and calculation of energy access indicators, demand forecasting through machine learning, and energy systems’ optimization and cost analysis. The approach involved five related components. Country-specific data was collected, analysed, and used to define an energy profile. This profile was then applied as input to a smart metering experiment using a variable household electrical load and a smart meter to measure electricity usage, from which data was collected on General Packet Radio Service (GPRS) communications via Meter Data Management (MDMS) software. The resulting energy data was analysed on its applicability to the SEAP framework and explored over three exercises that included the analysis and calculation of energy access indicators, demand forecasting through machine learning, and energy systems’ optimization and cost analysis. The measured household energy data, analysed and explored using tools and platforms that include Python, Azure ML Studio, and Homer Pro, were directly or indirectly applicable to all assessments in the SEAP framework and exposed the possibility of generating additional data for further use on applications that require a specific range of datasets. These capabilities presented the potential for energy planners and policymakers to use improved data to determine the indicators for the implementation and monitoring of an energy access program; furthermore, it unlocked aspects of data forecasting and optimization of energy systems in terms of sizing and cost.