Smarter than smart. Cognitive buildings that can think for themselves?

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1 Smarter than smart. Cognitive buildings that can think for themselves? Dr Claire

2 Historically Human beings spend 90% of their lives indoors. Today Global Real estate is valued at over $250 Trillion, with $7 Trillion added annually. This represents 13% of global GDP, increasing to 15% in Tomorrow Smarter Building Investments will reach $30BN annually by 2022 with estimates of over a billion sensors deployed. 2

3 Real Estate Trends in Europe: pwc & ULI 3

4 Why? $30 billion US by 2022 The amount building AI market is expected to grow by PropTech Sector is disrupting and maturing.. Are you keeping up? 36% 2.5% Global energy consumption is from the building sector Annual increase global use of electricity in buildings 500 Million IOT devices have already been installed in buildings Legislation NZEB, Global & National CO 2 reductions etc Source: Experience Technology improves experience

5 As companies re-envision how they conduct business, their people and products still need places to go. We need to help clients optimize their portfolios through three areas: Operational Excellence Smart Buildings Workplace Experience

6 The Internet of Things: Digitizing the physical world Deloitte (2016) Yesterday, the value of commercial real estate was all in location. Tomorrow, much of it will be in information..

7 The empowered generation 59% Percentage of millennials that value state-of-the-art workplaces* Vision Rising generation of millenials eagerly looking to expand their power and voice. Technological advances will make it easier to try/fail and co-create without boundries. Highly competitive labour markets. Directions Social space making building users part of something bigger Home will dictate places of work Gamification to motivate engagement & learning Never one size fits all personalization of the physical environment. 7 *Source: Oseland & Burton Building the Business Case, Facilities Management 2013, quoted at:

8 As sectors disrupt each other 8 Group Name / DOC ID / Month XX, 2017 / 2017 IBM Corporation

9 Future technologies converge at fast pace forging new ecosystems Vision Virtual reality, robotics, nanotechnology, 3D printing etc. will drive radical economic changes: value chains will fragment, industries will converge, and new ecosystems will emerge. Challenge Forging technology partnerships to enable rapid and continous change Identifying new business models and revenue streams from data Knowing where competition comes from Robotics with a human feel Anticipating how augmented reality impacts on Real Estate

10 The Challenge

11 How do you scale the Internet of Buildings? For multinationals, building energy management happens on a global scale One building can easily provide several thousands of data points Solutions must scale to multiple sites and buildings distributed globally Large amounts of data need to be integrated and processed

12 The Convergence of IoT Architectures Cloud-centric Systems (Computing in Cloud) Edge-Centric (Computing on Edge) Fog-Centric (Computing everywhere) Cloud Edge Field IoT Device Edge Analytics Knowledge Security

13 The Analytic Barrier Most Boring! Forbes*: Data preparation most time-consuming (80%) and least enjoyable (76%) data science task 75% of IoT providers say that data analytics is the most in-demand skill set they look for in candidates Platforms become so powerful and scalable that the human becomes the bottleneck to perform specific tasks like data cleaning which requires domain knowledge. Wisdom Knowledge Information Integration Barrier Data 80 % of time* Data quality issues Lack of knowledge how data is related Lack of temporal, spatial, organizational context Lack of meta-data of available data * Forbes: Cleaning Big Data (2016)

14 The challenge in IoT We have: Billions of different IoT devices We want: Analytic workflows distributed across the fog? 14

15 Example problem statement We are drowning in feeds, legacy data, and spreadsheets, with no centralization of my data and manual data entry, resulting in time wastage, boring jobs ad high attrition My building data is telling me too little too late, causing me to miss targets and savings opportunities. Trial and error has driven more error than success So we find ourselves reacting to whatever / whomever makes the most noise, resulting in lack of trust in the systems and poor operational performance 15 Watson / Presentation Title / Date

16 The Answer..?

17 Building Management Evolution Automated Buildings ( ) Smart Buildings ( ) Cognitive Buildings (> 2015)

18 Putting AI to work Machine Learning Natural Human interaction Artificial Intelligence Reason insights & actions 18 Watson IoT / Building Insights / July 2018

19 Energy Management without AI How can I detect abnormal behaviour? How can I resolve abnormal behaviour? Building Data? Analysing energy data is promising to reduce building operation expenses. But, energy consumption is influenced by many aspects and it is hard to detect and diagnose abnormal consumption: Manual approaches require experienced operators and scale poorly Rule-based approaches require deterministic behaviour and maintenance Statistical approaches do not consider exogenous variables Data mining models are black-box models that are hard to interpret and trust

20 From Learning, Predicting and Diagnosing Energy Flow 1) Monitor & Model 2) Predict & Detect 3) Diagnose & Explain

21 to usable Energy Insights Consumption analytics and diagnostics for Energy consumption & prediction Energy health of estate Asset savings and wastage detection Data quality analysis Energy impact of weather AI models for energy prediction Predicted energy consumption AI models for diagnosis Energy waste cost avoidance Overall energy savings in a building today and YTD Overall energy wastage today and YTD

22 Components of a Cognitive Building Machine Learning and Prediction for Energy Reasoning of Physics for Diagnosis Knowledge Graphs for Data Management Natural Language Conversation via Watson Assistant Deep Learning for Occupancy

23 Example: Thermal Comfort Diagnosis 6 Buildings with a total of 3,500 sensors Causes Rules Sensors Sensors Rules Causes Old System New System Old System New System The approach covers 12 times more sensors than the old system Ref.: Semantic Diagnosis Approach for Buildings. IEEE Trans. Ind. Inf., 2017

24 Where can this take us?

25 Inform participants my of ETA. Best transport option GETTING HERE Information on other colleagues in the building Information on who I am meeting Where am I meeting? Automatic security access ARRIVING Self parking car ENTERING Host in reception My space, my preferences: Free meeting room Noise levels, traffic, light, temperature WORKING HERE Send me a coffee to my desk What is the nutritional content of today s menu DRINKING AND EATING Eat in or out you tell me based on my calendar, my movement, my dietary requirements Impromptu meeting - require noisy space Tell me the best time to leave the office Check me out with security LEAVING Tell me the fastest route home QR code on mobile device to allow access to relevant areas Security cleared Where are my colleagues Way find BEING HERE Free desk Lift or stairs? Tell me which type of space works best to deliver this meeting objective COLLABORATION Who globally has the skillsets to assist? Connect us.

26 Endless Use Cases and Opportunities DIGITAL ASSET LIFECYCLE The manual input of data into a maintenance system for thousands of assets is a Ferrovial: 3% - 7% TLC savings costly, error prone process. REAL TIME ASSET LOCATION Finding assets and their relevant data is challenging, in particular when you IBM: are in the field. 33% - 50% FM savings COGNITIVE ASSET HEALTH Predictive and preventive maintenance are immanent to reduce operation costs, but, lack the required sensors. SELF-LEARNING ENERGY DIAGNOSER Energy consumption has many influences and it is hard to detect and diagnose abnormal consumption. Tesco: 20M Energy savings COGNITIVE CONCIERGE Guiding people to their rooms and answering their questions is an central element of hospitality. SEMANTIC INSIGHTS FLUID SPACES MY COGNITIVE CAMPUS MY ARTEFACT SCALABLE IOT PLATFORM Analyzing thousands of IoT devices cannot provide meaningful insights without a semantic method. The lack of real-time occupancy information within a building inhibits Public Sector: 27% per head saving effective energy and space performance. Increase productivity of teams by providing a comfortable environment and easy navigation. Maintaining the perfect environment for art galleries and museums is critical. Cognitive Buildings require a highly scalable platform for data integration and analysis.

27 Cognitive Concierge You can ask me anything.

28 Diagnose Assets with Augmented Reality Sam, Maintenance Worker Just let me fix the problem. Hand me my wrench

29 Where is this going...?

30 The outcomes? Combined use Personalization Autonomous control Well-being Flexible multi-use buildings Enhanced experience Real time knowledge of full lifecycle costs Increased profits. 30

31 Thank you & Questions? 31 Watson / Presentation Title / Date