Experimental validation of an electrical and thermal energy demand model for rapid assessment of rural health centers in sub-Saharan Africa

Rapid deployment of health service infrastructure is underway to meet the growing needs of populations in sub-Saharan Africa, however the energy infrastructure needed to support high quality services has tended to lag. Understanding the electrical and thermal energy needs of health centers constructed with local building methods and materials and operating outside of the jurisdiction of heating, ventilation and air conditioning (HVAC) codes is complicated by a lack of appropriately scaled and configured energy system design frameworks and validation data for dynamic simulations. In this work we address this gap by linking the thermal envelope performance of health center buildings under heating and cooling loads with measured indoor air temperature, meteorological conditions, and operational electricity demand. A resistance-capacitive type energy balance model is parameterized using typical health center architectural data for sub-Saharan Africa (floor plans from Uganda and Lesotho) and heat transfer characteristics; to achieve this energy flows between HVAC equipment, internal loads, and ambient conditions are simulated on an hourly time step with indoor temperature thresholds representative of thermostat settings. A typical meteorological year dataset for Lesotho is used as a case study, validated with indoor temperature measurements and power metering at four health center sites spanning a daily patient load ranging from 15 to 450 per day over rural and urban communities. High resolution electricity measurements from smart meters installed at the clinics are used to close the energy balance and form the basis of a probabilistic method for forecasting long term hourly electricity demand in African health centers. These data and the corresponding method have relevance to energy system design for health clinics across sub-Saharan Africa, especially those featuring intermittent renewable generation. The integration of these two modeling approaches constitutes a novel tool for sizing and costing energy infrastructure to meet operational demand at health centers in both urban and rural areas of developing countries.