This research article published by the Journal of Electrical Systems; Vol. 15, Iss. 4, (2019)Most emerging countries such as Tanzania are promoting rural electrification through installation of microgrids. This paper proposes an approach for short-term day-ahead load forecast in rural hybrid microgrids in emerging countries. Energy4Growing research project by Politecnico di Milano department of energy in collaboration with EKOENERGY (www.ekoenergy.org) implemented in Ngarenanyuki Secondary School (Arusha, Tanzania) innovative control switchboards to form an energy smart-hub. The smart-hub was designed to manage the school’s 10kW hybrid micro-grid comprising: PV-inverter, battery storage, microhydro system, and genset. Ngarenanyuki school microgrid’s data was used for the experimental short-term load forecast in this case study. A short-term load forecast model framework consisting of hybrid feature selection and prediction model was developed using MATLAB© environment. Prediction error performance evaluation of the developed model was done by varying input predictors and using the principal subset features to perform supervised training of 20 different conventional prediction models and their hybrid variants. The objective function was feature minimization and error performance optimization. The experimental and comparative day-ahead load forecast analysis performed showed the importance of using different feature selection algorithms and formation of hybrid prediction models approach to optimize overall prediction error performance. The proposed principal k-features subset union approach registered low error performance values than standard feature selection methods when it was used with ‘linearSVM’ prediction model. Furthermore, a hybrid prediction model formed from the elementwise maximum forecast instances of two regression models (‘linearSVM’ and ‘cubicSVM’) yielded better MAE prediction error than the individual regression models fused to form the hybrid.