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Marine energy (ME) has the potential to power businesses in the blue economy. Kelp farms are an emerging maritime market of the blue economy and are predicted to grow, but they are not currently using ME for their power needs. As the number and size of kelp farms increase, more offshore power will be needed onsite for operations, monitoring, and harvesting. ME devices such as tidal current energy converters and wave energy converters (WECs) may be used to supply power for these needs. This article assesses the status of kelp farming in the continental United States, investigates the electricity needs of kelp farms, and examines the feasibility of generating the required electricity from wave and tidal current energy. The United States currently has 165 kelp farms that have either active or pending permits. The farms use electricity for boat operations, kelp drying, environmental monitoring, offshore lighting, and the raising and lowering of lines. Most kelp farms are in protected, nearshore waters that do not have significant wave energy resources. The limited available wave energy could be used to power small devices, but WECs have not yet been developed for that application. Some kelp farms are in locations that feature significant tidal energy resources, but small tidal current energy converters that are compatible with existing farm operations are not yet commercially available. As low-power WECs and tidal current energy converters are developed, kelp farms could be research partners and early adopters of the new technologies, which would encourage their broader use by other blue economy businesses. [ABSTRACT FROM AUTHOR]

The prediction of wave height and flux of energy is essential for most ocean engineering applications. To simultaneously predict both wave parameters, this paper presents a novel approach using short-term time prediction horizons (6h and 12h). Specifically, the methodology proposed presents a twofold simultaneity: 1) both parameters are predicted by a single model, applying the multi-task learning paradigm, and 2) the prediction tasks are tackled for several neighbouring ocean buoys with such single model by the development of a zonal strategy. Multi-Task Evolutionary Artificial Neural Network (MTEANN) models are applied to two different zones located in the United States, considering measurements collected by three buoys in each zone. Zonal MTEANN models have been compared in a two-phased procedure: 1) against the three individual MTEANN models specifically trained for each buoy of the zone, and 2) against some state-of-the-art regression techniques. Results achieved show that the proposed zonal methodology obtains not only better performance than the individual MTEANN models, but it also requires a lower number of connections. Besides, the zonal MTEANN methodology outperforms state-of-the-art regression techniques. Hence, the proposed approach results in an excellent method for predicting both significant wave height and flux of energy at short-term prediction time horizons. [ABSTRACT FROM AUTHOR]

Global climate change has prompted many national plans for rapid emissions reductions. For example, the United States recently committed to transitioning to 100% carbon-free electricity by 2035 and net-zero emissions economy-wide by 2050. Parallel to conversations surrounding emissions reductions is the call for energy justice, or the demand for more equitable distribution of energy-related burdens and benefits among communities. To date, energy justice has evolved as a mostly academic conversation, which may limit its utility to praxis. In response, we offer an interdisciplinary framework that aims to organize existing knowledge and lessons learned from energy development. Specifically, we developed the Meaningful Marine Renewable Energy (MRE) Development Framework and conducted a literature review using MRE as a case study. MRE was chosen because it is a nascent renewable energy technology in the US with projects mostly in demonstration stages and no commercial deployment, making it a useful case study to apply lessons learned from other energy sectors and other countries. Discussion of current resources being developed among the MRE community and their implications for furthering energy justice priorities are also explored. We conclude the review with a compiled list of questions meant to support stakeholders in translating theoretical concepts of Meaningful MRE Development to practice. Although the Meaningful MRE framework was developed using MRE as a use case, our interdisciplinary theoretical framework can be applied beyond MRE to other sustainable and renewable energy projects. • Energy Justice as an academic conversation may limit its utility to praxis. • Meaningful MRE development framework emerges from literature review and theoretical synthesis. • The Meaningful MRE development framework is adaptable to other energy projects. • Community-level data accessibility are critical components of energy justice. • Guiding list of questions may help to engage the community and promote trust. [ABSTRACT FROM AUTHOR]

Abstract: Since the turn of the 21st century, the onshore wind industry has seen significant growth due to the falling cost of wind generated electricity. This growth has coincided with an interest in the development of offshore wind farms. In Europe, governments and developers have begun establishing small to medium sized wind farms offshore to take advantage of stronger and more constant winds and the relative lack of landowner conflicts. In the U.S., several developers are in the planning and resource evaluation phases of offshore wind farm development, but no wind farms are currently operational or under construction. In this paper, we analyze the patterns of development in Europe and compare them to the U.S. We find significant differences in the patterns of development in Europe and the U.S. which may impact the viability of the industry in the U.S. We also discuss the policies used by European nations to stimulate offshore wind development and we discuss the potential impacts of similar policies in the U.S. [Copyright &y& Elsevier]

Marine energy is an emerging renewable energy industry with the potential to produce 2300 terawatt-hours per year from resources within the United States. As development and testing of marine energy devices advance, regulatory and permitting decision-makers are concerned about the uncertainty surrounding the potential environmental effects resulting from the introduction of these novel devices in coastal and riverine environments. The Triton Initiative researches and provides recommendations for environmental monitoring technologies and methods to inform industry stakeholders with the data necessary to permit the testing of marine energy systems. Effective dissemination of the research findings is essential for improving the accessibility of data to stakeholders who may use the results to inform policy decisions, yet few frameworks for conducting science communications for marine energy projects exist. In this paper, we present tools, channels, and tactics for developing a science communication framework for marine energy projects, or similar areas of study, using the Triton Initiative's pilot science communication program as a case study. By leveraging existing bodies of work in disciplines such as communications theory, marketing, public relations, and social science, the presented framework includes audience identification and analysis; channel development, including a website, blog, newsletter, social media, and webinars and presentations; and metrics for determining success. Outcomes from one year of Triton's case study are presented, including the most effective tactics and lessons learned. [ABSTRACT FROM AUTHOR]

Wave-wind hybrid energy systems have become a recent focus due to the benefits they provide in terms of power reliability and long-term cost reduction for offshore power production. The efficiency of these systems partially depend on the wave and wind properties of the installation site. In this work, an unsupervised machine learning technique was used to classify the regions in the United States based on data provided by the National Data Buoy Center (NDBC). The aim was to create an initial assessment tool to improve decision-making in the design process for wave-wind hybrid systems with high adaptability within each region based on the recorded data. The data for all the weather stations was collected from 2010 to 2019. Three features of wave height, wave period and wind speed were used to cluster the regions during 2019, using the 72 stations with available data. The classification procedure was then repeated for the available data for five-year (2015–2019) and ten-year (2010–2019) periods. For the clustering purpose, the wave and wind properties for each year were considered as features. The characteristics of each cluster were discussed and the results were compared with the outcomes for the classifications based on one-year data. Further, detailed statistical information regarding each cluster were provided. • K-means clustering is used to classify the US coastal regions based on oceanic data recorded by NDBC. • The classifications are based on one-year data, five-year data, and ten-year data. • The classification procedure uses wave height, wave period, and wind speed as its features. [ABSTRACT FROM AUTHOR]