AI-SEHOS: A Solar-Powered, Energy-Aware Digital Learning Framework for Nomadic Communities in Northern Nigeria

Nomadic and pastoralist communities in Northern Nigeria are poorly served by digital-education strategies because electricity is absent, households are seasonally mobile and ICT infrastructure is weak. This concept paper specifies AI-SEHOS (AI-driven Solar Energy Harvesting, Optimisation and Storage), a four-layer framework (Data, Energy, AI Decision and Educational Service) that treats instructional demand and mobility as explicit terms in energy dispatch and load allocation. The specification uses twenty-five numbered equations adapted from established photovoltaic, forecasting, battery-estimation and reinforcement-learning work. The components were exercised on a documented synthetic generator with chronological splits, repeated runs and stated baselines; the code is supplied. On synthetic data only, long short-term memory (LSTM) and gradient-boosting forecasters reduced one-hour-ahead test RMSE by about 4–6% relative to smart persistence (nRMSE 2.52% and 2.56% of capacity versus 2.67%) and were statistically indistinguishable; the gain disappeared under a simpler generator. A tabular Q-learning dispatch policy matched, but did not beat, the best rule-based controller on 30 held-out traces. Criticality-aware allocation raised server availability during school hours from 81% to 98% and reduced weighted unmet load by about 34% under fixed reference weights, at the cost of more lighting shortfall; the allocation depended only on weight rank. These results illustrate mechanisms; they are not evidence of field performance. The paper contributes a specification, a reproducible test bed, a field-validation protocol with pre-stated hypotheses and an explicit account of what remains unestablished.