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This paper presents SAVE, for Stochastic Analysis and aVailability of Energy, an analytical framework providing closed form expressions for residual energy and lifetime prediction of wireless sensor nodes. SAVE models a wide umbrella of input factors, including channel characteristics, different energy sources and harvesting policies, link layer parameters (e.g., error control and duty cycling) and various data traffic generation models. Our framework uses stochastic semi-Markov models to derive the residual energy distribution for each harvesting node accounting for practically observed temporal variations. We validate the analytical expressions derived by SAVE by means of simulations, and show that SAVE predictions provide a remarkably close match to the simulation results.