Harnessing green energy such as wind and solar is key to sustainable economic growth. Energy storage plays an essential role due to the intermittent nature of these energy sources, and offers many advanced services otherwise impossible for traditional power distribution systems (e.g. energy shifting and peak shaving). The reliability and health of such an essential component of future smart grid is crucial. Thus, the system needs to gather continued operation sensory data to support autonomous decision making while minimizing early personnel intervention, a requirement of particular significance for sparsely populated areas in Canada. The challenge is the that a typical energy storage installation can have over a million battery cells generating an enormous amount of continuous sensor readings of voltage, current, and temperature. Our cloud-based solution is centered around a two-tier computation model, on-site and in-cloud. On site of energy storage systems, raw data are compressed to eliminate spatial and temporal redundancies before sending to the cloud. With sensory data collected network-wide, the in-cloud module builds models for anomaly detection and other intelligence services. Parameters of the trained model are then downloaded to the on-site computers periodically to configure their decision-support module. As such, on site of installations, decision-making is responsive using system-wide experiences and local sensory data. Meanwhile, with all sensory data compressed and uploaded to the cloud, the in-cloud module can iteratively improve its models for further improvement.