Big Data Analytics for Energy Efficiency

2020 ◽  
Vol 8 (10) ◽  
pp. 15-23
Author(s):  
Michail Angelopoulos ◽  
Christina Kontakou
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Customer Segmentation ◽  
Expert Advice ◽  
Program Participation ◽  
Organizational Challenges ◽  
Managing Change ◽  
Data Points

Industries and utilities explore more and more case studies and expert advice concerning the possibilities of intelligent business administration and Big Data analytics. The movement to integrate and meaningfully interpret huge tanks of data points is reaching beyond cutting edge utilities into the mainstream. This project aims at exploring the application of Big Data analytics in the energy sector. Within project’s framework, already developed and operational datasets will be combined with supplemental data from publicly available sources, including prior energy efficiency program participation, enrollment in other energy programs and services, geographic data, customer equipment profiles, demographics, and psychographic customer segmentation categories. There are significant technical, budgetary and project management challenges in undertaking the development and integration of Big Data analytics. There are also organizational challenges in managing change, making decisions in a cooperative framework, and grounding project goals and strategies in the customer experience. The ultimate goal of the project is to develop a reliable database for a step closer to the energy efficiency direction.

scholarly journals A survey and analysis of big data management based on computational methodologies

2020 ◽  
Vol 6 (20) ◽  
pp. 48-53
Author(s):  
Shweta Kumari
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Enterprise ◽  
Enormous Amount ◽  
Current Scenario ◽  
Data Points ◽  
The Impact ◽  
Day By Day

n a business enterprise there is an enormous amount of data generated or processed daily through different data points. It is increasing day by day. It is tough to handle it through traditional applications like excel or any other tools. So, big data analytics and environment may be helpful in the current scenario and the situation discussed above. This paper discussed the big data management ways with the impact of computational methodologies. It also covers the applicability domains and areas. It explores the computational methods applicability scenario and their conceptual design based on the previous literature. Machine learning, artificial intelligence and data mining techniques have been discussed for the same environment based on the related study.

Big Data In Banking: A Bird’s Eye View

GIS Business ◽  
2020 ◽  
Vol 14 (6) ◽  
pp. 1129-1139
Author(s):  
C. RADHA PRIYA ◽  
KANNIGA PRASHANTH
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Banking Industry ◽  
Big Data Analytics ◽  
Customer Segmentation ◽  
Customer Relationship ◽  
Risk Modeling ◽  
Customer Data ◽  
Customer Lifetime ◽  
Big Data Application

Banking industry is the backbone of any economy. It plays a very significant role in leading the country towards the growth path by improving the gross capital formation, which consecutively improves the GDP. Success of the banking industry depends on its ability to serve its customers efficiently and expeditiously. The functionality of the CRM (Customer Relationship Management) can be effectuated by felicitous use of customer data. Banks have voluminous data about their customers, which most of the banks failed to utilize in a well-timed manner. Banks can fortuitously satisfy their customers by offering much personalized and focused services by pursuing big data analytics and other hi-tech tools or applications. Big data analytics can be actuated in key areas like customer segmentation, offering customer lifetime value, fraud detection, risk modeling, etc. Preeminent banks in the industry are utilizing big data to leverage the accumulated customer data for improvising their services. Big data offers a promising scope of ventures to banks which consider it strategically. This article is attempts to present an overview of the big data application in the banking industry.

scholarly journals From Ethnographic Research to Big Data Analytics—A Case of Maritime Energy-Efficiency Optimization

2020 ◽  
Vol 10 (6) ◽  
pp. 2134 ◽  
Author(s):  
Yemao Man ◽  
Tobias Sturm ◽  
Monica Lundh ◽  
Scott N. MacKinnon
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Data Analytics ◽  
Management Practices ◽  
Big Data Analytics ◽  
Training Data ◽  
Ethnographic Research ◽  
Data Sets ◽  
Shipping Industry ◽  
One Year

The shipping industry constantly strives to achieve efficient use of energy during sea voyages. Previous research that can take advantages of both ethnographic studies and big data analytics to understand factors contributing to fuel consumption and seek solutions to support decision making is rather scarce. This paper first employed ethnographic research regarding the use of a commercially available fuel-monitoring system. This was to contextualize the real challenges on ships and informed the need of taking a big data approach to achieve energy efficiency (EE). Then this study constructed two machine-learning models based on the recorded voyage data of five different ferries over a one-year period. The evaluation showed that the models generalize well on different training data sets and model outputs indicated a potential for better performance than the existing commercial EE system. How this predictive-analytical approach could potentially impact the design of decision support navigational systems and management practices was also discussed. It is hoped that this interdisciplinary research could provide some enlightenment for a richer methodological framework in future maritime energy research.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

A Review on Big Data Analytics Tools in Context with Scalability

2019 ◽  
Vol 7 (2) ◽  
pp. 273-277
Author(s):  
Ajay Kumar Bharti ◽  
Neha Verma ◽  
Deepak Kumar Verma
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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