The quest for novel computing architectures is currently driven by (1) machine learning applications and (2) the need to reduce power consumption. To address both needs, we present a novel hierarchical reservoir computing architecture that relies on energy-efficient memcapacitive devices. Reservoir computing is a new brain-inspired machine learning architecture that typically relies on a monolithic, i.e., unstructured, network of devices. We use memcapacitive devices to perform the computations because they do not consume static power. Our results show that hierarchical memcapacitive reservoir computing device networks have a higher kernel quality, outperform monolithic reservoirs by 10%, and reduce the power consumption by a factor of 3.4× on our benchmark tasks. The proposed new architecture is relevant for building novel, adaptive, and power-efficient neuromorphic hardware with applications in embedded systems, the Internet-of-Things, and robotics.