Ocean energy data learning from big data

Author(s):  
Vikas Khare ◽  
Savita Nema ◽  
Prashant Baredar
Keyword(s):  
Big Data ◽  
Smart Science ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 175-183 ◽  
Author(s):  
Hsueh-Yuan Lin ◽  
Sheng-Yuan Yang

2021 ◽  
Vol 13 (23) ◽  
pp. 13322
Author(s):  
Vinoth Kumar Ponnusamy ◽  
Padmanathan Kasinathan ◽  
Rajvikram Madurai Elavarasan ◽  
Vinoth Ramanathan ◽  
Ranjith Kumar Anandan ◽  
...  

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.


Author(s):  
Mardhani Riasetiawan ◽  
Ferian Anggara ◽  
Ahmad Ashari ◽  
Sarju Winardi ◽  
Bambang Nurcahyo Prastowo

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Bokolo Anthony ◽  
Sobah Abbas Petersen ◽  
Dirk Ahlers ◽  
John Krogstie ◽  
Klaus Livik

AbstractThe smart grid achieves bidirectional information and energy flow between energy consumer and utility grid, aiding energy users not only to utilize energy, but also to produce, sell, and share energy with other consumers or with the utility grid. This type of energy user is referred to as the “prosumer”. Thus, prosumer management structures are important within energy market. However, prior studies on energy sustainability has paid little attention on prosumer involvement and management. Likewise, the continuous growth of cities has increased data processing complexity. Consequently, processing and analysis of historical, online, and real-time streaming data from energy sensors and metering devices has become a major issue in smart cities. Therefore, this research aims to present an architecture based on big data to improve energy prosumption in smart community districts by applying enterprise architecture approach grounded on The Open Group Architecture Framework (TOGAF). Accordingly, qualitative methodology is adopted to collect data by employing case study by focus group interview from two energy companies in Norway to preliminarily validate the architecture. Findings from the case studies was demonstrated in ArchiMate modeling language to evaluate the applicability of the architecture. Moreover, findings from this study provides practical scenario that energy service providers can refer to in designing their own energy data platforms. Essentially, the architecture can be utilized as a guide to help municipalities and policy makers in creating approach for energy data analytics in smart community districts towards making decisions for future energy prosumption planning.


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