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Offshore wind is one of the most fascinating industries in the renewable energy sector and it is experiencing a remarkable growth. Offshore wind energy generation offers an opportunity in the race to decrease the dependence on fossil fuels, reduce green house emissions, increase energy security and create employment opportunities. UK has proven success in offshore wind and has been enjoying the economic benefits of offshore wind since over a decade. Offshore wind energy is an emergent renewable energy industry in the United States. The United States is coping up with the challenges and heading up fast to catch up with the industry. India is still in its infancy stage where the policy frameworks are framed by MNRE government and getting ready with the tools to enter into the offshore market. This paper researches the current situation and trend of offshore wind industries in UK and US, from aspects of policy, grid connections, operation and maintenance and cost reduction and analyses the proper direction and pathways of the industry to India. Therefore this paper highlights the scenario as to how these three countries UK, USA and India, respectively, are enabling offshore wind, to make a vital and sizeable contribution to the low carbon economy. [ABSTRACT FROM AUTHOR]

Abstract Wind speed and power forecast is an essential component to ensure grid stability and reliability. The traditional forecasting methods fail to address the non-linearity in the wind speed time-series, thus paving way for machine intelligent algorithms. This paper discusses a hybrid machine intelligent wind forecasting model utilizing different variants of Support Vector Regression (SVR) built on wavelet transform. Various performance indices are evaluated to identify the possible best one among four different machine learning regressors for wind forecasting application. Apart from standard ε-SVR and LS-SVR, two new regression models, namely, ε-Twin Support vector regression (ε-TSVR) and Twin Support vector regression (TSVR) are used to forecast short-term wind speed, and are compared with Persistence model for four wind farm sites. The effect of larger dataset on forecasting performance is evaluated for two wind farm sites from USA and India. Further, wind power ramp events are investigated at different hub heights and the forecasting performance of different variants of SVR is compared for five wind farm sites. Highlights • Machine intelligent SVR variants are discussed for short-term wind forecasting. • Overfitting and underfitting scenarios of variants of SVR are assessed. • Forecasting performance of SVR variants is assessed during wind power ramp event. • Wind power ramp events are analyzed at different hub heights for 5 wind farm sites. [ABSTRACT FROM AUTHOR]