S. Vasavi <br/><br/>
Correlation analysis of offshore wind and wave power potential at Indian exclusive economic zone during 2014–23 using deep learning model 

 - P 269-282 
<br/><br/> Climate change is increasingly influencing energy<br/>resources across the globe, and its effects on renewable<br/>energy sources like offshore wind and wave power are<br/>becoming crucial topics of study. India, with its extensive coastline and vast exclusive economic zone (EEZ),<br/>has significant potential for harnessing these oceanbased renewable energies. By analysing the localised<br/>nature of offshore winds and their sensitivity to climate variations, we can improve predictions of future<br/>wind power output. Therefore, to sustain wind energy<br/>development within India’s EEZ, it’s essential to evaluate the region’s wave energy potential and its vulnerability to climate change. This paper investigates the<br/>potential for offshore wind energy within the Indian<br/>EEZ and assesses its vulnerability to climate change.<br/>Spatial and temporal wave data such as wave period<br/>and wave height are collected from Copernicus Marine<br/>Data Store to generate the wave power layer and validate the proposed U-Net model. For improvement<br/>of data quality, assimilation techniques such as the<br/>Kalman filter and Bilateral filter are used. For finding<br/>the wave power density hotspot region, the semantic<br/>segmentation is performed using an enhanced U-Net<br/>model. The model archives an impressive IoU score of<br/>82.66%, conforming its accuracy to identify the wave<br/>power density hotspots. To analyse the impact of climate change on wave power potentials, the Pearson<br/>correlation technique is used to correlate between<br/>ocean surface salinity and ocean surface temperature.<br/>The r value of correlation between ocean surface temperature and ocean surface salinity ranges from –0.59<br/>to –0.0 and indicates a weak, moderate inverse relationship, the positive range varies from 0.01 to 0.62,<br/>suggest that a weak to strong positive correlation,<br/>where both temperature and salinity tend to increase<br/>together. In the case of temperature and wave power<br/>density, there is a negative correlation from June to<br/>October, influenced by seasonal temperature variation<br/>due to rainfall and it effects to correlate with wave<br/>power density. 
<br/><br/><label>Subjects--Facted Index Terms: </label><br/>Exclusive economic zone<br/>Kalman filter<br/>U-net model<br/>wave energy potential<br/>wave power density hotspots