Big Data Processing for Renewable Energy Telemetry Using a Decentralized Cloud M2M System

Abstract

Hydro-, Aeolian and Solar energy show significant promise in helping to reduce pollutants and greenhouse gases, which is a primary focus in today's sustainable development culture. To enjoy the power of them, the human society needs solutions to transform the energies mentioned above into electric energy, at specific reliability, efficiency and sustainability parameters. Maintaining this kind of parameters implies, among other things, the careful and permanent monitoring of equipment. The monitoring process implies remote monitoring, as we are talking about preserving natural resources. The equipment is mostly situated in the middle of nature, covering large areas mostly outside of populated locations. In their great majority installations for wind and solar energy have been designed and manufactured relatively recently, which makes them contain systems of tele-monitoring that are designed and included by default, as a part of great importance to the entire investment. The situation is different in the case of hydroelectric plants, which have a tradition of over 130 years and have been and are still built to this day. Renewable energy sources are being increasingly used and need to be constantly monitored for optimizing the power grid. Unfortunately, such micro power plants are located in difficult to access remote locations where often only satellite or sparse GSM radio signals are available. In this paper we study the way how to process big data gathered by a & George Suciu decentralized cloud system, based on general systems and remote telemetry units (RTUs), for tele-monitoring of renewable energy objectives. Also, we analyze a proposed cloud M2M system, where each RTU communicates by radio with a telemetry data gateway connected to a cloud computing infrastructure equipped with appropriate software that delivers processed data. Furthermore, we present how we use a search based application built on Exalead CloudView to search for weak signals in big data. In particular, given the telemetry application, we propose to leverage trivial and non-trivial connections between different sensor signals and data from other online environmental wireless sensor networks, in order to find patterns that are likely to provide innovative solutions to existing problems. The aggregation of such weak signals will provide evidence of connections between renewable energies and environmental related issues faster and better than trivial mining of sensor data. As a consequence, the software has a significant potential for matching environmental applications and challenges that are related in non-obvious ways. Finally, we present the measurement results for a hydro-energy case study and discuss the applicability for other renewable sources such as solar or wind energy.

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