Abstract
A fast-expanding number of oil & gas companies are planning to shift investment to renewable energy projects, with offshore wind expected to spearhead this campaign. Now, with global demand for wind power growing, major oil and gas companies are diversifying their portfolios by developing offshore wind and the companies that provide services to offshore fossil fuel platforms are seeing a new market rising in their wake. Changing the future service life of oil and gas platforms by converting them into support structures for offshore wind turbines requires some retrofitting measures in order to reduce the cost of maintenance, increase reliability and improve the structural performance of the integrated turbine-platform system. Five different retrofitting solutions are presented, four of which are related to the substructure and one is related to a modified wind turbine. The retrofitting solution related to the substructure was the only solution that guaranteed safety. This study dealing with the retrofitting activities is the continuation of a previous work where the repurposing of oil and gas platforms for offshore wind energy generation was investigated.

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Capturing complex spatio-temporal features of thousands of correlated taxi-demand time-series in the city makes the traffic flow prediction problem a challenging task. Hence, several Deep Neural Network (DNN) models have been developed to mimic the latent spatio-temporal behaviour of taxi-demand time-series in a city to improve the prediction results. Despite, good performance of recent DNN based traffic prediction techniques, such models can only identify either adjacent or connected regions with direct or transitive connection; hence they fail to capture spatio-temporal correlation among regions that exhibit implicit or latent connection. Additionally, the dependency of the recent DNN models on recursive components facilitates error propagation during feature aggregation without any counter strategy for it. In view of these existing glitches, we introduce a novel DNN model, graph Multi-Head Convolution for Spatio-Temporal Aggregation (gMHC-STA) which supports capturing spatio-temporal correlation among regions with explicit and implicit connection both. Moreover, gMHC-STA aggregates both spatial and temporal characteristics using multi-head attention; thus overriding recursive RNN or its variant approach to prevent noise propagation. The experimental results of gMHC-STA on two real-world city taxi-demand datasets report minimum of 6.5–10% improvement over the best state-of-the-art on standard benchmark metric in varying experimental conditions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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