Book

Africa’s energy future hinges on three things: better forecasts, better materials, and faster deployment. Data-Driven Clean Energy for Africa delivers all three. Bridging atmosphere and alloy, this book shows how modern machine learning turns climate data into accurate solar PV forecasts and how high-temperature thermoelectric materials convert wasted heat into dependable watts. With reproducible workflows, case studies from West Africa, and clear metrics, it equips planners, utilities, researchers, and funders to cut uncertainty, lower costs, and improve reliability. Readers learn end-to-end PV forecasting pipelines (ANN/SVM\CNN), robust validation (RMSE, nMAE, skill vs. persistence), and hybrid portfolio design using complementarity indices. On the materials side, the book demystifies transport physics, substrate compatibility, and geometry-driven efficiency for Ho–Sb–Te and related systems, connecting lab performance to device and module design. The result is a practical playbook—from weather to watts—that turns data and materials science into real power for communities, mini-grids, and national networks alike. Who it’s for: energy planners, utilities/IPP engineers, development agencies, graduate students, and anyone building reliable, clean power in emerging markets.