Whitepaper_Transforming the Energy and Utilities Industry With Smart Analytics_Aug%272015

Transforming the Energy & UUliUes Industry with Smart AnalyUcs Page 2

Executive SummaryAccording to market forecasts, energy and utility industry players were planning to invest US$7 billion on big data and analytics in 2014 which represents one of the highest cross-industry spending at 15% (ABI Research, 2014). Given an annual average CAGR of 25%, the same figure is expected to treble to US$21 billion in another four years time. Key business drivers behind the adoadoption of energy data analytics include cost reduction through demand response and generation programs, personalized customer care, the growing complexity of customer data and an increasing need to comply with regulatory norms.

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. This white paper will showcase how energy and utility providers can unlock potential service opportunities using opportunities using a reliable predictive analytics solution across all stages of the business cycle.

SMART METERMILESTONES

The British government hasannounced an ambitious plan to install smart meters for each and

every household by 2020

(Sou(Source: gov.uk)

70 - 72%

The roll-out target for smart metersin the European Union

for 2020.

(Source: Euractiv.com)

50%Smart meters already installed all

across the US representing

43%... of all household

(Source: Green Tech Media)

BackgroundWith utility companies announcing smart meter roll-outs in their millions (see infographic), consumers in the near future will be in greater control of their domestic usage patterns and exercise greater freedom of choice on which utility provider should eventually get their business. Clearly, there’s an ongoing transition happening from predictable business models to a cucustomer-centric model where utilities have to concentrate on end user experience.

Figure 1: Smart Meter Statistics


It has to be remembered that other industries such as telecom have successfully undergone this transition, largely by integrating analytics across their upstream and downstream processes. Utility players are clearly in the throes of unique opportunities and stepping up to the plate on customer expectations is the only way forward to addaddress business challenges.

The onset of smart meters marks a watershed on the future direction of the energy business. Pushed by increasing competition, cost issues, deregulation, varying consumer choice and environmental compliance, utility players worldwide have shifted from the supply-side business model to a data-driven, demand-based appapproach. It makes more business sense to exercise real time controls over power generation, consumption and costs rather than blindly augmenting capacity or surprise end users with bill shock. Capturing proactive intelligence on consumer behavior not only helps stretch the dollar through more frugal resource allocation, it also helalso helps utilities stay ahead of the game by leveraging consumption patterns to land end users smack dab in the middle of lucrative pricing models and tariff offers.

However, it’s not difficult to see that the utilities aren’t doing much with the widely available data since they don’t have the knowledge set to convert the data into meaningful business intelligence. For instance, according to the Utility Analytics Institute each year, rebates worth millions of dollars are not communicated in time to the cuto the customers because most utilities struggle to keep track of information emerging at customer end. An informed business decision can only be taken when the “disaggregated” data is somehow used to develop tailor-made products and services to design a sales funnel for long-term growth.

The key to pulling together all the disparate information into a systematic revenue-generation strategy requires the input of big data analytics. Using predictive, statistical models based on historical inputs, utilities are able to create predictable load forecasting and innovative pricing plans which differentiate their offerings frfrom competition. They can also identify boundary case and corner case scenarios (outages, sudden spikes in power consumption) and realign their entire business operations around rich data insights.

At each and every node, the data generated at smart meters should be accessible to business heads for speedy integration into creative business application scenarios.

It has been estimated that predicting demand response can lead to up to 90 per cent cost savings compared to other alternatives of generation capacity.

Compared to any other point of time in history, utility providers have access to the largest data set of consumer behavior which according to a research by Edison Foundation, stands at more than 1 billion data points on a daily basis – these information bytes include wattage, demographics and even device-specific power consumconsumption.

Current Challenges andApplication Scenarios

PREDICTING DEMAND RESPONSE


In order to predict demand response, utilities should be able to integrate a wide stream of information including weather data, individual, non-intrusive meter readings at 15 minute intervals along with the historical record of high peak demands.

A typical cost of operating a medium-sized thermal power plan of 500 Mw capacity is $250,000 per day. Increasingly, utilities are planning their per day generation for various target markets well ahead of time based on a load forecast of power demand. Clearly, predicting generation response will go a long way in accumulaccumulating supply side cost savings. Renewable energy players can predict their own generation response based on weather factors such as ambient and dew point temperatures, cloud cover, wind speed and precipitation.

BI-based loyalty programs without increasing tariffs.

In order to transform BI data into actionable intelligence, utility players need a unifying energy management framework which can remotely and precisely capture at each and every stage. A predictive analytics system, hosted either on cloud servers or local servers, drives the necessary logical, computational and statistical alalgorithms to analyze large volumes of event data, e.g. correlation, data clustering, regression analysis. The various stages of the BI/analytics are represented in the diagram shown in next page.

Utility companies receive source side data through sensor integration at various points of the energy flow matrix starting with power generation comprising both coal-based thermal power plants as well as renewable energy sources.

AAt the distribution stage, each and every transformer, substation and electric pole is fitted with an intelligent electronic device which transfers data to a wireless mesh network. Smart meters available at end user location as well as new sensors transfer the final information to the Cloud using protocols like ANSI C12.18; IEC 61107, Open Smart Grid POpen Smart Grid Protocol (OSGP) and TCP/IP communications.

A BI/Analytics Frameworkto Visualize Energy Data

• Data Collection

PREDICTING GENERATION RESPONSE

More and more utilities are facing pressure from environmental groups and governments to comply with norms which endeavor to simplify the accessibility of utility reports at the end user level. As per government regulations, utilities will now have to provide customers with detailed information about their electricity consumption papatterns pertaining to time of day, device usage and more.

REGULATORY COMPLIANCE

With deregulation in effect and the widespread availability of smart meters, customer expectations are undergoing a phenomenal improvement. Utility providers are in a race against time to offer personalized plans and

REDUCING CUSTOMER CHURN


Here are a few examples of data records that can prove useful in driving monetization use cases:

Fire/smoke alarms

Floods

Weather-driven network outage

Voltage dip/short circuit/overload

Peak demand

Temperature control -- HVAC (heating, ventilation and air conditioning)

Smart home alarms

Figure 2: BI/Analytics Framework for Smart Energy Analytics

DATA ANALYSIS

DATAVISUALIZATION& REPORTING

Energy OptimizationFramework

Data to ActionableIntelligence Flow

Collection

ImproveInteractivity

PrioritizeResponse

AutomateAction

Map Alarm / Predict Failure

PredictPatterns

Discovery

Aggregation

Analytics &Visualization

VisualizationService

AdvancedAnalytics

AnalysisPlatform


Alarms and failures have to be mapped very precisely using a meter analysis tool application which includes a collection of visualization widgets to explore and uncover irregularities within energy and other metered utilities. Demand patterns have to be created based on real-time data integration with weather and geoggeographical locations.

In order to transform BI data into actionable intelligence, utility players need a unifying energy management framework which can remotely capture data at each and every stage. To explain the performance benefits of smart analytics, we will review an Internet of Things deployment performed by eInfochips for a leading smart homes utility plhomes utility player. Smart homes were installed with sensors, smart meters, thermostats and integrated security devices with the Control Unit running on Zigbee and Wi-Fi. Events were monitored for a month, 6 months and 1 year. The following figures will help explain data volume and velocity in a smart homes scenario.in a smart homes scenario.homes scenario.

• 1million devices installed X 100 events per device per day = 100 million records a day • The events that occurred in 1 Month, 6 months and 1 year• 100 million records per day X 30 days = 3 billion records a month• Time needed to individually profile 3 billion records = 2 minutes x 3 billion = 10000 years!

Performance Benefits andMonetization – Smart HomeUse Case

The applications are designed to integrate data from countless programs and systems for the utilities including real estate, work order, energy spend and utilities use. The data is then brought together in one place and normalized so it can be used by various applications across multiple vendors and types of devices. The analyzed data is then deliis then delivered to two types of applications: facility data applications and business intelligence tools for further processing. Predictive Analytics tools like MongoDB and Reporting tools like Tableau enable pattern creation on the demand side with an aim to improve overall distribution.

In order to derive actionable intelligence flow for aggregated data, the analytics protocol leverages various tools including geospatial processing, complex events processing, classification, machine learning, forecasting and multidimensional processing. At this stage, it is possible to visualize the answers to all possible 5W que5W questions.

• Data Analysis

At this stage, the analytics system should improve interactivity through a graphical, map-based, high-level overview of the whole real estate portfolio along with the ability to drill down to individual regions or facilities for more focused views of performance. To enable prioritized response, the operations dashboard organizes perperformance information in prioritized order so that it’s easy to see which efforts will have the greatest impact tied to specific goals, and with the least amount of effort. In order to automate action, the operations dashboard offers a simple workflow for responding events and alerts.

• Data Visualization and Reporting


With a smart analytics capability percolating to each and every consumer household, utility providers are in a better position to offer demand response services based on individual usage pattern. This puts the end user in control of their actual consumption. Based on forecasted power consumption situations, utility providers can warn their their consumers about their peak demand well ahead of time encouraging them to save on energy expenditure thus, reducing their energy bills, eliminating bill shocks and black-out situations.

Potential Business Areas forUtility Players

Instead of 100 million, the MongoDB tool inserts only 1 million records and retrieves 300,000 which contributes to saving valuable time, costs and productivity in data analysis (see below). Sample reports are captured with monthly view for real and false alerts, number of devices being captured, state wise threat alerts and installations to alarm to alarm ratio.

Figure 3: Smart Retrieval using MongoDB

Figure 5: Don't Waste Time in Query Processing!

Figure 4: Pre-aggregation of Data inMongoDB for Faster Query Processing

DEMAND RESPONSE SERVICES

Coincidental peak demand prices are higher than peak demand charges and have seasonal patterns, these charges not being in control of individual consumer but a large number of consumers. Utilities have access to operational demand response programs to generate historical data enabling accurate prediction of coincident peak demand with clearly articulpeak demand with clearly articulated short and long-term patterns.

Coincident Peak Predictions can be further improved using factors like weather, renewable supply such as Solar PV system.

COINCIDENTAL PEAK DEMANDPROGRAMS

There are two kinds of expenditure models: Power Supply model and Power Demand model. The former consists of public grid, local power and renewable power generation and sits at the

EXPENDITURE MODELING ANDCOSTING


eInfochips’ smart homes framework comprises of a data integration module with events and device data processing performed by MongoDB and an analytics module with Tableau used for creating charts and reporting.

eInfochips Smart EnergyAnalytics Solution

• Smart Homes

eInfochips’ SMAC framework intelligently integrates application, device, online and

• SMAC

As a MongoDB IoT solutions partner, utility players can apply eInfochips’ software and system development capabilities to deliver operational insights, reduce costs, improve customer service and create new revenue streams. MongoDB greatly helps in text search and aggregation with full, flexible Index support and rich queries helping perhelping perform analysis in real time. Our IoT framework encompasses design expertise in small form factor, lower power wearable objects, wireless SoC and IoT gateways, prototyping boards and platforms, integration with sensors, rich web and mobile apps, analytics and BI.

• Internet of Things

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, Spring security framework. Through web access, the administrator can manage all connected devices frfrom a single interface.

• Cloud Enablement

heart of consumer demand response algorithm for consumers participating in CPP (Coincidental Peak Program). In contrast, the Power Demand model consists of flexible and non-flexible continuous and batch type workloads.

unstructured data from all entry points, and performs predictive analytics by integrating with Hadoop, CRM/ERP and MongoDB. It also provides custom service BI and ad hoc reporting using Tableau and integrates on Cloud/SaaS framework over mobile apps, custom web and desktop apps, Google Computing engine, Amazon Web Services, MicMicrosoft Azure and other high end systems.Using smart analytics tools, utilities can predict

future resource demand originating from historical data thus, preparing a sequence of load patterns for all given locations. This helps avoid service disruptions as well as overcapacity concerns which can greatly reduce energy expenditure for users from anything between 20 to 30%.to 30%.

eInfochips offers smart analytics solution support for the energy and utility industry across development, QA, reengineering and sustenance cycles with IoT, M2M connectivity and device/sensor integration and video and data analytics integration. We also perform custom application development for alerts and controls. Our Our expertise in energy/utility sector touches across the following areas:

PREDICTING RESOURCE DEMAND

Utility players are struggling with a deluge of intelligent customer data which will hold the key toward their future business growth. It has to be added that without resourceful analytical tools and strategic partners, making sense of customer data is analogous to finding a needle in a haystack. Potential business growth areas in revealing business insights are astounding: utility players can leverage smart analytics to create price packages and custom deals similar to those being offered by telecom carriers such as AT&T, Verizon and Sprint. They can further diversify their revenue streams by delivering high value in emevalue in emerging growth areas such as Internet of Things, clean energy solutions and smart grids, and collaborate with smart home system players to capitalize on consumer demand for intelligent homes. In summary, the usage of smart analytics is a win-win for both utilities and their customers.

www.einfochips.com | [email protected]

eInfochips is a Product Engineering Solutions company recognized for leadership by Gartner, Frost & Sullivan, NASSCOM and Zinnov. eInfochips has contributed to 500+ products for top global companies, with more than 10 million deployments across the world. The company is debt-free and profitable since inception in 1994.

HQ: Sunnyvale, CA, USATel: [email protected]

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Whitepaper_Transforming the Energy and Utilities Industry With Smart Analytics_Aug%272015