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3 3 Multiple building 145 structures 15M Sqf office systems & lab space 58,400 housed heads $55M annual utility spend 2M connection points megawatt hour average Watch video here Microsoft 500M transactions per day "Give me a little data and I ll tell you a little. Give me a lot of data and I ll save the world." Darrell Smith, Director of Facilities and Energy, Microsoft

4 Developer drives innovation, sustainability and productivity through smart-energy facilities Challenge Automate and simplify management of 300 buildings Measure and reduce energy consumption Support environmental sustainability initiative Solution Enhance monitoring, management, and coordination of operations across all facilities Leverage IoT and big data to improve building management Benefits Improved operations, equipment reliability, & proactive issue identification Our partners should use the fault information such as priority, location, and fault age to focus on equipment that needs attention. This will also result in increased productivity and higher efficiency for our partners. Yim Cheng, CIO, JTC

5 Connecting Buildings to the Cloud for a Greener Planet Challenge Solution Benefits With increased volume, Johnson Controls wanted an easier, more automated way to collect data and provide detailed intelligence on systems running in any location worldwide. Create a platform capable of integrating building components like sensors, thermostats, rooftop air handling and chillers systems using Azure IoT Suite and Cortana Intelligence Suite. Avoids more than $300,000 in hourly downtime costs for manufacturing customer Decreases energy costs for a more sustainable environment Provides an extensible platform for end-to-end building management Chillers account for about 50 percent of the energy consumption in a building. It s higher than transportation or any other industry, so being able to run chillers efficiently makes a big difference. Sudhi Sinha, Vice President, Product Development, Johnson Controls

6 30 Azure regions available today Hyper-scale footprint 777 T Storage Transactions per day 60B Hits to Websites 4 T Internet of Things messages per week 80% Of Fortune 500

7 Energy & Utilities Partners Win 10 IoT Core Microsoft Azure IoT Suite Connect Collect Store Process Learn Visualize Command Control Manage Secure

8 COMPONENT DIAGRAM GATEWAY & BROKER PROCESSING & STORAGE Service API PRESENTATION PERSONAS This architecture illustrates these key technologies and the flow of data between them. Alarms/Notifications Device State Notification Hub Tenant To better show how this works, the fully composed diagram at right is described in two sections that correspond with key subscenarios represented in the Devices Stream Analytics Filtering/Normalization Cache DB API App Mobile Service Facilities Manager overall scenario: service alert and service delivery. Field Gateway IOT Hub Data Lake Azure Blob/Tables Azure ML Data Warehouse Operations Engineer Each section is itself parsed into its fundamental use cases, one building on the other, more or less sequentially. Website Power BI Building Technician Devices Building Management & Integration System HDInsight (Hadoop) Logic App INTEGRATION SERVICES Enterprise Integration Hub VNET EXTERNAL DATA SOURCES CORE BUSINESS APPLICATIONS IDENTITY/ACCESS MANAGEMENT ON-PREMISES SYSTEMS Azure AD Connect Other (e.g. utilities) Weather Forecasts Dynamics AX Dynamics CRM Online & FieldOne Skype for Business Office 365 Azure AD

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10 Connected Field Services

11 Analytics & Machine Learning

12 Anomaly detection example Detecting leaks in water surge tank system Results Identified slow leak in distribution system before it turned into a bigger leak (and potentially causing damage) Fixing leak saved water and energy (running surge pump to refill tank) Results Clustering example Identifying who s in a building and where Increased understanding of how buildings are used Supported improved real-estate build-out (conference rooms, offices, shared spaces) Improved operations and maintenance procedures (scheduling inspections/cleaning more often in highly used spaces) Improved security procedures 12

13 Predictive maintenance example Scheduling maintenance of heavy equipment when warranted Results Regression example Predicting energy demand Potential reduction of frequency of maintenance for lightly-used equipment Potential reduction of equipment failures by scheduling maintenance based upon survival estimates using ML and real-time operating data Potential reduction of unscheduled equipment downtime Potential reduction of lifetime maintenance costs Results Energy demand values based upon tracked environmental parameters Ability to run what-if scenarios 13

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15 2016 Microsoft Corporation. All rights reserved. Microsoft, Azure, Cortana, HDInsight, PowerBI, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.