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Smart technology has potential in tracking the key challenges based on population based on the sustainable future. In today's cultures, a smart approach enables for the integration of information needed to address crucial concerns. The critical challenge is to limit the effects of global warming while maintaining a balanced economic growth. The impact of global warming mitigates the fundamental problem while ensuring the balance economic development. Intense research efforts should be directed toward balanced resource utilization, renewable energy system integration, efficient energy conversion technologies, effective process integration, effective techniques to enable a circular economy framework, and other issues that are important to the population. This paper finds the latest technology in the field of smart grid technologies which focused on the effective enhancement and efficient utilization of resource. The issues and challenges in using sustainable future are discussed and bring new ideas towards the sustainable future base on the application of smart technologies. [ABSTRACT FROM AUTHOR]

Renewable wind power is productive and feasible to manage the energy crisis and global warming. The wind turbine's blades are the essential components. The dimension of wind turbine blades has been increased with blade sizes varying from approx. 25 m up to approx. 100 m or even greater with a specific purpose to increase energy efficiency. While wind turbine blades tend to be highly stressed by environmental conditions, the wind turbine blade must be constantly tested, inspected, and monitored for wind turbine blades safety monitoring. This research presents a methodology adaptation on machine learning technique for appropriate classification of different failure conditions on blade during turbine operation. Five defects were reported for the diagnosis study of defective wind turbine rotor blades, and the considered defects are blade crack, erosion, loose hub blade contact, angle twist, and blade bend. The statistical features have been drawn from the recorded vibration signals, and the important features was selected through J48 classifier. Eight tree-dependent classifiers were used to categorize the state of the rotor blades. Among the classifiers, the least absolute deviation tree performed better with the classification percentage of 90% (Kappa statistics = 0.88 , MAE = 0.0362 , and RMSE = 0.1704) with a computational time of 0.06 s. [ABSTRACT FROM AUTHOR]