A B S T R A C TOcean energy is regarded as an important future source of energy generation in many countries for transition to a low-carbon future. While commercial interest in ocean energy is growing significantly at a global level, there are considerable investment costs and bottlenecks that will need to be overcome. Research and funds are spread over many different wave and marine current energy concepts under development, and there is still no technology convergence, in contrast to what happened in wind energy. Although an important marine energy resource, discussion of offshore wind energy is not included in this manuscript. This article focuses on the latest developments in ocean energy-in particular, open-sea testing facilities set up by several countries as a measure to encourage deployment and streamlining procedures-and gives an overview of projects going into the water this past year. In addition, the article highlights the importance of collaborative research and development on ocean energy projects and the unique role of the Ocean Energy Systems Implementing Agreement as an intergovernmental organization promoting the use of ocean energy (wave, marine currents, tidal, ocean thermal gradients and salinity gradients) for energy extraction.
Decision-making is a complex and demanding process often constrained in a number of possibly conflicting dimensions including quality, responsiveness and cost. This paper considers in situ decision making whereby decisions are effected based upon inferences made from both locally sensed data and data aggregated from a sensor network. Such sensing devices that comprise a sensor network are often computationally challenged and present an additional constraint upon the reasoning process. This paper describes a hybrid reasoning approach to deliver in situ decision making which combines stream based computing with multi-agent system techniques. This approach is illustrated and exercised through an environmental demonstrator project entitled SmartBay which seeks to deliver in situ real time environmental monitoring.
The utilization of a stream analytical processing approach is presented in the context of real-time analysis of acoustic data streams from hydrophones for cetacean identification and classification. We discuss the development and interim results of an integrated platform for processing underwater acoustic data in real time that also utilizes supplemental data types related to the physical environment. This approach provides advanced capabilities such as real-time dynamic filtering to support the intelligent processing of multiple high volume continuous data streams in parallel. The stream processing platform is being employed for the development of a continuous broad spectrum monitoring station to establish background levels for underwater noise for environmental impact assessment and continued operational monitoring for underwater acoustic noise produced by wave and tidal ocean energy devices. The use of hydrophone and particle velocity detectors in conjunction with other real-time sensors and data sources with dynamic feedback and control mechanisms is presented. The monitoring technologies are discussed along with the establishment of a consistent measurement methodology for varied deployment regimes dependent on water depth, bottom conditions, and variable sea states.We also review the incorporation of these technologies in the Marine Institute of Ireland's multipurpose research and development SmartBay Galway system to provide a flexible and agile monitoring and management platform which is being extended to include additional environmental variables specific to the ocean energy domain. The platform is being developed for the Galway Bay Quarter Scale Wave Energy Test Site and the full-scale, gridconnected Atlantic Marine Energy Test Site now under development by the Sustainable Energy Authority of Ireland.
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