Streaming applications come from various application fields such as physics, where data is continuously generated and must be processed on the fly. Typical streaming applications have a series-parallel dependence graph, and they are processed on a hierarchical failure-prone platform, as for instance in miniaturized satellites. The goal is to minimize the energy consumed when processing each data set, while ensuring real-time constraints in terms of processing time. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption, and we ensure a reliable execution by either executing a task at maximum speed, or by triplicating it, so that the time to execute a data set without failure is bounded. We propose a structure rule to partition the series-parallel applications and map the application onto the platform, and we prove that the optimization problem is NP-complete. We design a dynamic-programming algorithm for the special case of linear chains, which is optimal for a special class of schedules. Furthermore, this algorithm provides an interesting heuristic and a building block for designing heuristics for the general case. The heuristics are compared to a baseline solution, where each task is executed at maximum speed. Simulations on realistic settings demonstrate the good performance of the proposed heuristics; in particular, significant energy savings can be obtained.