Big data analytics has the potential to address and improve several issues in the power sector – operational, strategic, and financial, among others.
Cloud computing, machine learning, Internet of Things (IoT), robotics, blockchain and cybersecurity are being deployed by utilities in their operations. These are the big current and future investment avenues for utilities, and they all have one thing in common: they generate huge amount of data. And the data generated is growing each year as more and more smart devices and technologies are being deployed within the power systems infrastructure by utilities as well as consumers. Much of the utilities’ infrastructure is becoming smart – meaning that it has built-in processing, connectivity, and sensing capabilities. Electric vehicles (EVs), smart home systems, grid management systems, and many more subsystems are likely to interface with utilities and provide them with potentially valuable data.
The challenge for utilities is to make this data useful and generate actionable insights from it. Benefiting from big data is not straightforward and utilities need to deploy a range of new IT solutions that allow them to collect the data in consistent ways, transport it, secure it, analyze it and store it. Solutions that manage big data in the industry need to quickly make sense of data from multiple sources and in diverse formats. Several big data analytics products and platforms are already being used in order to harness this data.
Currently only a small number of utilities have adopted big data analytics actively. It is mostly the largest utilities in the world and very few smaller utilities that have embraced the technology. While some of these utilities have designated other companies to implement big data in their businesses, some have collaborated with technology companies through partnerships and joint ventures to build new products and services specific to the power sector that can be sold to other utilities.
This report explores the usage of Big Data and analytics in the utilities industry.
The report discusses the latest trends and developments in the field of big data and how these affect utilities. The report further gives a brief analysis of the big data industry and lists several use cases and case studies for big data in utilities.
The report also explores the value chain of the big data industry and lists the companies implementing big data applications in the utility sector.
Reasons to Buy
The report provides:
A comprehensive analysis of the present scenario and emerging market trends in the global big data industry.
Insights of the global market leaders and challengers in the big data industry and where do they fit in the value chain.
Extensive analysis of the applications of big data in power utilities.
Profiles of major market players within the big data industry, which aid in interpreting the competitive outlook of the big data industry.
Duke Energy, Enel, EDF, National Grid, E.On, Exelon, SGCC, TEPCO, KEPCO, Innogy, Vattenfall
Table of Contents
TECHNOLOGY BRIEFING 5
Data trends 7
Technology trends 8
Data center trends 11
Macroeconomic trends 12
Regulatory trends 12
Utility trends 13
INDUSTRY ANALYSIS 14
Growth in big data is being driven by the increased use of broadband internet among several other factors 14
Big Data and IoT in Utilities 15
Mergers and acquisitions 15
USE CASES 19
Predictive Maintenance 19
Data driven consumption management 19
Optimization of V2G systems based on EV charging, parking, and retail consumption data 19
Dynamic Line Rating 19
CASE STUDIES 20
Vestas using IBM’s big data software and hardware to pinpoint ideal coordinates for wind turbines within a farm 20
Enel uses GE’s Predix platform to draw insights from big data analytics 20
Southern Company and Schneider’s Prism technology 20
ROMEO project to use big data and AI to reduce offshore wind O&M costs 20
VALUE CHAIN 21
Big data generation 21
Big data management 21
Data governance and security 22
Business intelligence 22
Data analysis 22
Data storage 23
Data processing 23
Data aggregation 24
Data integration 24
Big data product development 24
Big data consumption 24
Public companies 25
Private companies 31
Utilities implementing big data 33
APPENDIX: OUR THEMATIC RESEARCH METHODOLOGY 39