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A pilot project was implemented in Arusha\u2010Tanzania; it mainly comprised of a PV\u2010inverter and a lead\u2010acid battery bank connected to the local electricity utility company, Tanzania Electric Supply Company Limited (TANESCO). A very short\u2010term power outage prediction model framework based on a hybrid random forest (RF) algorithm was developed using open\u2010source Python machine learning libraries and using a dataset generated from the pilot project\u2019s experimental microgrid. Input data sampled at a 15\u2010minute interval included day of the month, weekday, hour, supply voltage, utility line frequency, and previous days\u2019 blackout profiles. The model was composed of an adaptive similar day (ASD) module that predicts 15 minutes ahead from a sliding window lookup table spanning 2 weeks prior to the prediction target day, after which ASD prediction was fused with RF prediction, giving a final optimised RF\u2010ASD blackout prediction model. Furthermore, the efficacy analysis of the short\u2010term blackout prediction of the formulated RF, ASD, and RF\u2010ASD regression and classification algorithms was compared. Considering the stochastic nature of blackouts, their performance was found to be fair in short\u2010term blackout predictions of the test site\u2019s weak grid using limited input data from the point of coupling of the user. The models developed were only able to predict blackouts if they occurred frequently and contiguously, but they performed poorly if they were sparse or dispersed.<\/jats:p>","DOI":"10.1155\/2022\/4763820","type":"journal-article","created":{"date-parts":[[2022,9,9]],"date-time":"2022-09-09T02:05:06Z","timestamp":1662689106000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Very Short\u2010Term Blackout Prediction for Grid\u2010Tied PV Systems Operating in Low Reliability Weak Electric Grids of Developing Countries"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8099-7298","authenticated-orcid":false,"given":"Benson H.","family":"Mbuya","sequence":"first","affiliation":[]},{"given":"Aleksandar","family":"Dimovski","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Merlo","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4539-6021","authenticated-orcid":false,"given":"Thomas","family":"Kivevele","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2022,9,8]]},"reference":[{"key":"e_1_2_9_1_2","volume-title":"Africa energy outlook","author":"International Energy Agency","year":"2019"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.worlddev.2019.104635"},{"key":"e_1_2_9_3_2","unstructured":"Rea andN. 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