Estimating hourly lighting load profiles of rural households in East Africa applying a data-driven characterization of occupant behavior and lighting devices ownership

To design energy access solutions for rural households in developing countries it is important to have an accurate estimation of what their electricity consumption is. Studies reveal that they mainly use electricity to meet their lighting needs, as they cannot afford high power-consuming appliances. However, the scarce data availability and modeling complexity are a challenge to compute correctly the load profiles without collecting data on-site. This paper presents a methodology that computes the hourly lighting load profiles of rural households in East Africa requiring a small amount of publicly available input data. Combining data from household surveys, climate, and satellite imagery, the methodology applies machine learning for determining occupant behavior patterns, and lamps ownership for indoor and outdoor usage. For this, an average prediction accuracy of 80% is reached. After applying lighting requirement functions, load profiles are generated and then validated using measured data from 13 households in Kenya. Results show that the methodology is able to compute the load profiles with an average normalized root mean squared error of 0.7%, which is less compared to existing simulation approaches using on-site data. To demonstrate a broad application, the monthly lighting consumption is computed and projected geospatially for households in Kenya.