In the present work, the offshore wind and wave energy resource in the Norwegian waters is assessed. The analysis is based on recently released ERA5 reanalysis wind and wave database, which covers a period of 41 years (1979-2019). For the assessment, several statistical features have been used, such as seasonal variability, annual and inter-annual variability, quantiles of the probability distribution, directional distribution, to mention some of them. In addition, the possibility of combined use of the two renewable energy sources has been examined by analyzing their joint correlation structure. On the basis of the analysis findings, suitable areas are identified as a suggestion for possible energy harvesting. This is a key initial step towards the recently set EU goal of reaching more than 60 GW of offshore wind energy capacity and more than 1 GW of ocean energy capacity in Europe by 2030.


There is an abundant renewable energy potential in the oceans. Renewable energy harnessed from the oceans can contribute to the reduction of CO2 emissions and other applications related to blue economy (Pecher and Kofoed, 2017). Among ocean energy resources, offshore wind and wave energy experience great interest because of their potential sustain-ability and deployment (Magagna et al., 2016; IRENA, 2020).

Thus, in the pre-commercial phase, it is very important to have a reliable assessment of the potential of both ocean energy sources. This, in turn, will help one to get a better insight for selecting appropriate locations, as well as a better estimate of their cost-effectiveness.

Especially, for the combined exploitation which is expected to further reduce costs, only few papers are present (Pérez-Collazo et al., 2015; Kalogeri et al., 2017).

Since the pioneering work of Isaacs and Seymour (1973), various assessments have been published both on a global scale (Krogstad and Barstow, 1999; Barstow et al., 2008; Cornett, 2008; Arinaga and Cheung, 2012; Reguero et al., 2015) and on a regional scale (Stopa et al., 2013b; Liang et al., 2014; Soukissian et al., 2017; Silva et al., 2018; Allahdadi et al., 2019), using satellite and/or model data.

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