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Whitepaper - Transforming the Energy & Utilities Industry with Smart Analytics

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Check out this white paper from eInfochips which showcases how energy and utility providers can unlock potential service opportunities using our predictive analytics solution across all stages of the business cycle. Major utility players are set to roll out millions of smart meters with the aim of generating actionable insights even though as per the industry’s own admission, any serious effort toward monetization is being offset by a lack of core IT capabilities, especially in big data technology. Capturing proactive intelligence on consumer behavior is the way to go. In this white paper, eInfochips demonstrates how utility players can predict demand response, generation response and create new revenue models around coincidental peak demands, smart expenditure modeling and other forms of end user data.

Published in: Engineering

Whitepaper - Transforming the Energy & Utilities Industry with Smart Analytics

  1. 1. 2 Why Utility Players Need BI/Analytics - Energy and utility industry players to invest US$7 billion on big data and analytics in 2014, cross-industry spending at 15% (ABI Research, 2014) - At an annual CAGR of 25%, the same figure is likely to increase by three times to US$21 billion by 2019 - Key business drivers: Demand response and generation programs, personalized customer care, complex customer data, regulatory norms The Challenge? Utilities lack core IT capabilities in unlocking value data from millions of smart meters and converting them into business models
  2. 2. 3 Smart Meter Era – Need for Change in Utility Business - Watershed moment: Utilities worldwide have announced smart meter roll-outs in their millions - Transition from predictable business models to a customer-centric one - Transition from supply-side business model to a data- driven, demand-based approach - Which is better? - Option A: Real time controls over generation, consumption and costs OR - Option B:Blindly augmenting capacity and surprising end users with bill shock
  3. 3. 4 The Challenge of “Disaggregated” Data! - Compared to any other point of time in history, utility providers have access to the largest data set of consumer behavior – over 1 billion data points daily - “Disaggregated” data of no use to utilities unless convertible to revenue models The Solution? Utility Players are looking to leverage Big Data-based predictive, statistical models derived from historical inputs to create predictable load forecasting and innovative pricing plans
  4. 4. 5 New Utility Revenue Models based on BI/Analytics - Predicting demand response can lead to up to 90 per cent cost savings compared to other alternatives such as increasing generation capacity - Predicting generation response will go a long way in accumulating supply side cost savings - Meeting regulatory compliance - Reducing customer churn: Providing BI-based loyalty programs for customers without increasing tariff
  5. 5. 6 eInfochips’ BI/Analytics Framework to Visualize Energy Data - Data Collection: Fire/smoke, floods, weather outage, peak demand, HVAC, smart home data - Data Analysis: Predictive analytics on Normalized to create demand-side patterns - Data Visualization: Alarms and failures mapping, pattern prediction, prioritized responses
  6. 6. 7 Monetization Example – Smart Home Use Case - 1 million smart devices generate 3 billion records a month - Time needed to individually profile 3 billion records = 2 mins. X 3 billion = 10,000 years! The Solution? A tool like MongoDB inserts only 1 million records and retrieves 300,000 saving valuable time, costs and productivity in data analysis
  7. 7. 8 Potential Business Areas for Utilities - Demand Response Services: Putting the end user in control of their actual consumption - Coincidental Peak Demand Programs: When lots of consumers are involved, utilities can guide them to save on energy bills based on accurate prediction of coincidental peak demand - Predicting Resource Demand: Using smart analytics tools, utilities can predict future resource demand originating from historical data thus avoiding service disruptions as well as overcapacity concerns.
  8. 8. eInfochips Smart Energy Analytics Solution Smart Homes SMAC Internet of Things (IoT) Data integration module MongoDB – Events and device data processing integration Tableau – Charts and Reports for visualization Intelligently integrates application, device, online and unstructured data As a MongoDB solutions partner, eInfochips deliver operational insights, reduces costs, improves customer service and creates new revenue streams Cloud Enablement Predictive analytics using Hadoop, MongoDB, CRM/ERP BI and ad hoc reporting over Tableau Operating across public, private and hybrid cloud environments, eInfochips supports device integration across millions of device endpoints with security features like role-based access, HTTPS and crypto- secure tokens and Spring security framework. Areas of Expertise
  9. 9. To learn more on smart energy analytics and the eInfochips solution, please visit the link below to download our white paper http://einfochips.viewpage.co/smart-energy- analytics-for-utilities-whitepaper Read our White Paper
  10. 10. eInfochips Work Portfolio 11 Application System Hardware Mechanical BI and Big Data Cloud Mobility e-Commerce Firmware Operating System BSP and Drivers On-Chip Features Electronic / PCB FPGA ASIC and SoC IP Integration Enclosures CAD Modelling Ergonomics Industrial
  11. 11. Thank You 12 marketing@einfochips.com

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