Marine renewable energy as a power source for ocean observation applications has the potential to allow longer deployment operations due to the consistent, higher, and denser energy available from this resource. This additionally could encourage deployments in remote locations where maintenance is costly or resource availability is low if dependent on solar power. More importantly, gaps in spatial data could be filled. This paper examines the feasibility of a modular horizontal pendulum wave energy converter to power National Data Buoy Center's Self‐Contained Ocean Observations Payload (SCOOP) off the coast of Washington State, U.S. The effect on power output was studied when the pendulum's radius arm, mass, and power take‐off damping were varied. Results using Matlab toolbox WEC‐Sim revealed positive correlation between radius arm length and mass to power output, where power maximised for optimal damping values. Seasonal trends in power were not significant, where a 20 kg pendulum mass was needed to meet the SCOOP base power requirement of 5 W throughout the year.

Although many studies have validated wave energy converter (WEC) numerical models against scaled prototype experimental data, there remains a notable lack of validation using data from full-scale deployed WECs. This paper compares two numerical models of Monterey Bay Aquarium Research Institute’s Wave Energy Converter (MBARI-WEC), a two-body point absorber with an electro-hydraulic power take-off system (PTO). The models are implemented in WEC-Sim/Simscape and Gazebo Simulator. A statistical analysis of the models was performed, and field results were obtained to compare the models’ accuracy in predicting the RMS piston velocity, RMS motor speed, and mean electric power compared to field data for 56 observations across varying sea states. The Gazebo model demonstrated a closer agreement across all three parameters for a majority of the observations. When compared to the field data, the Gazebo and WEC-Sim models exhibited average mean electric power overestimations of 13% and 22%, respectively.