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Optimise Buildings with AI
- 1. Brad Middleton brad@uk.ibm.com November 2018 Optimize BuildingsWithAI: Predictive Energy Optimization
- 2. 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 2020. Watson IoT / Building Insights / June 20182 Tomorrow Smarter Building Investments will reach $30BN annually by 2022 with estimates of over a billion sensors deployed. Historically Human beings spend 90% of their lives indoors.
- 3. Operations consume 70% of the total cost of ownership (TCO) for a facility. Of that, the largest component is utilities; with commercial buildings consuming nearly 40% of Total U.S. energy 26% 24% 17% 18%8% 7% Utilities Repairs and maintenance Administrative Cleaning Security 26% Utilities 24% Asset Maintenance and Repairs 25% Administration and cleaning Source: BOMA 2016 Office Experience Exchange Report (Office EER) Watson IoT / Building Insights / July 20183
- 4. The Evolution of Building Management Automated buildings (1980 – 2000) Smart buildings (2000 – 2015) Cognitive buildings (> 2015) Phased investment has evolved infrastructure, rate of change is accelerating Visualize KPI Good for ratings Allows identifying general issues Bad for identifying energy waste Analyze energy consumers Understand consumption of rooms and central assets Only primary datapoints are analyzed Learn behavior Predictive control down to desk level Understand energy flow and building occupancy Consider comfort preferences of users Collect context such as weather and meetings Too many data points even for advanced analytics Watson IoT / Building Insights / July 20184
- 5. Light Utilities Air quality Noise Hot desks Power consumption HVAC Meeting rooms Temperature Smart meters Energy optimization Staff productivity Space occupancy and optimization Predictive maintenance Energy optimization is a leading smart building demand Aggregate and store data Watson IoT / Building Insights / July 20185
- 6. The challenge in IoT ? We have: Billions of different IoT devices Buildings creating Zetabytes of data We want: Analytic workflows distributed across the fog Watson IoT / Building Insights / July 20186
- 7. Putting AI to work for Energy Management Artificial Intelligence Augmented Intelligence Machine Learning Natural Human interaction Reason insights & actions Watson IoT / Building Insights / July 20187
- 8. Energy Management without AI ⎯ 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 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: ?Building Data How can I resolve abnormal behaviour? How can I detect abnormal behaviour? Watson IoT / Building Insights / July 20188
- 9. 9 “We can’t make sense of our smart building data.” • Drowning in data - We have invested in sensors and smart meters but the is raw and not meaningful. • Skill siloes - Our energy managers aren’t data scientists and our IT professionals aren’t energy experts. • Data siloes - We have multiple legacy systems that do not communicate each other or new IoT technology. • Enterprise scalability - Only one building is ‘smart’ or a few floors our buildings are connected, but we know how to scale. • False positives / manual changes – We have a rules-based energy that sends unreliable alerts and manual adjustments. • New investment – We’ve made significant investments in new designed for performance not energy efficiency. • Spreadsheet jail – Reporting on energy consumption is time only occurs quarterly or annually, and doesn’t provide detailed insights to make impactful decisions. Pain point caused by energy management without AI
- 10. Introducing IBM IoT Buildings Insights Creating Proactive Insights with AI and IoT Watson IoT / Building Insights / July 201810 Understanding Get full view of all buildings, assets, and sensors across entire enterprise Reasoning Know which assets are consuming excessive energy, as well as when and for how long the spikes occurred Learning Know which buildings are consuming the most energy and use AI to predict future consumption Make a long term investments that can scale and extend to any building, IoT technology, and system
- 11. Great… another offering… What is it? 11 Watson IoT / Building Insights / July 2018
- 12. 12 A fully cloud based offering that connects: Legacy building systems (BMS, IWMS) IoT sensors External solutions in every building across an entire enterprise. IoT Building Insights is… Responsive web application that… displays AI driven data insights in an user-friendly interface, which building owners and operators can leverage within improvement action plans to reduce costs and improve occupancy experiences. The first release of this application focuses on energy.Watson IoT / Building Insights / July 2018
- 13. Yeah…OK, that’s what it is…BUT, what does it do?13
- 14. Collect Client building sensors connect and send data to Watson IoT Platform Lighting Chiller HVAC Refrigerators Connect A knowledge graph of client buildings and assets with KITT semantic metadata layer Understand External Systems Any energy sensor connected to a main meter or sub meter Key Capabilities Release 1.0 IoT Partners, e.g. Weather Company Building Management Systems Visualize Release 1.0 Store & Archive Data is securely stored and archived Learn Analytical models learn the knowledge graph, identify anomalies, and predict energy usage IBM IoT Building Insights Watson IoT / Building Insights / July 2018
- 15. 15 AI…oh no! What do I need to know about that?
- 16. IoT Building Insights works by… Creating a knowledge graph of each building in an enterprise… https://www.youtube.com/watch?time_continue=2&v=aj9dU3vtWiQ Using AI models to diagnose and predict abnormal behavior in the building and its systems… Watson IoT / Building Insights / July 2018
- 17. Enterprise level view Know which buildings are consuming excessive energy Learn if the current energy consumption of the building is more than the same 30 days during previous year Based on current issues and previous predictions, know how much energy is being wasted Predict if energy consumption will increase or decrease in the next 48 hours Know if any sensors or smart meters are not sending data Easily view and drill down into individual buildings consuming excessive energy Watson IoT / Building Insights / July 2018
- 18. Building level view Know which assets, e.g. refrigerator, HVAC, etc. are consuming excessive energy Learn how long the unusual behavior occurred, could indicate ‘spikes’ Know how much the building is contributing to total enterprise consumption Learn if the current energy consumption of the building is more than the previous year Based on current issues and previous predictions, know how much energy is being wasted Predict if energy consumption will increase Know if any sensors are not sending data Easily view and navigate to other buildings consuming excessive energy Watson IoT / Building Insights / July 2018
- 19. IBM IoT Buildings Insights allows you to… Measure real-time energy consumption and run diagnostics ⎯ Overall energy health of real estate on any date in last month, last year or previous year ⎯ Average energy consumption for all sites v/s average energy consumption for specific site ⎯ Detailed information on metering outage / site outage ⎯ Energy consumption across entire enterprise and specific building ⎯ A correlation of energy consumption with weather data ⎯ Energy consumption status of the enterprise on a map, with alerts to focus on sites that need attention ⎯ Details of assets causing excessive energy consumption Access AI models for energy prediction ⎯ Predicted energy consumption across enterprise for next 48 hours and next month ⎯ Predicted energy consumption at specific for next 48 hours and next month Get Prescriptive Information on energy waste and cost avoidance ⎯ Overall energy savings across enterprise and a building for last month and last six months ⎯ Overall energy wastage in enterprise and a building for last month and last six months Watson IoT / Building Insights / July 201819
- 20. Strong investment in montioring infrastructure of their stores to make them more energy efficient 160 Stores 110.956 Data Objects 678TB Data per annum £20M savings pa 20% reduced cooling Case Study: Large Grocery Store Watson IoT / Building Insights / July 201820
- 21. Building Knowledge Graph Out-of-the-box integration with TheWeather Company AI models predictive analytics One common foundation for IoT use cases; interoperability with legacy hardware, BMS, BAS Offering Differentiators Watson IoT / Building Insights / July 2018
- 22. What is IoT Building Insights? A cloud based platform that connects legacy building systems, modern sensors, and external products AND a responsive web application that reveals AI driven insights in all buildings across an entire enterprise. 22 IoT Buildings Insights puts AI to work How does the AI work? It creates a knowledge graph of each building, which understands large datasets of concepts and objects in a building and how they are related, then AI models use the knowledge graph, as well as data coming from IoT sensors and building systems to detect and predict abnormal behavior. What does it do? It connects data from sensors, securely stores and archives it, creates a knowledge graph with that data and other data coming from external sources and building management systems, uses AI models to learn from the data, and presents visualizations of the data analysis and predictions from the AI models. Watson IoT / Building Insights / July 2018
- 23. CONNECT with us #1 #2 REACH out to your IBM contact #3 What should I do NEXT? [put your own twitter/linkedIn account here] ASK questions now #4 CHECK us out https://www.ibm.com/us-en/marketplace/iot-building-insights Watson IoT / Building Insights / July 2018
- 24. IBM IOT Building Insights Thank you
Editor's Notes
- Lighting accounts for nearly 20% of electric bills in commercial buildings and price increase 2-3% CAGR
- IBM believes that automated and smart buildings are increasingly giving way to cognitive buildings. In the 1980s and 1990s, building automation allowed real estate and facility management teams to visualize their buildings’ key performance indexes through dashboards. This helped to understand overall trends, and allowed better rating of buildings for energy reporting, for example. However, these dashboards were static, historical and aggregated, and did not provide actionable insight. They could tell us which buildings produced most waste, but not why, or what to do about it. As technology has matured and the penetration of instrumentation and analytics has increased, smarter buildings have become the norm. In the period 2000 – 2015, it has been possible to link sensor specific information with analytical tools to create actionable insights at the room and asset-specific level. However, as it is only possible to analyze primary data points, and as few organizations have implemented tools to be able to analyze large amounts of unstructured data, insights are still at an aggregate level and limited to comparisons with historical metrics. IBM believes that the era of cognitive buildings is now here. Cognitive buildings autonomously integrate IoT devices and learn system and user behavior to optimize performance and have three core capabilities: The ability to provide insight, from applying advanced analytics to near real-time data from IoT sensors and instrumented devices, enriched with contextual information from application programming interfaces (APIs), such as weather data. The ability to learn, reason with purpose and interact naturally with humans, with speech and text. Buildings learn normal operational patterns based on weather, occupancy and history, and can autonomously identify and diagnose abnormal patterns and recommend an action to be executed by a human, robot or system. The recommendation is added to the knowledge base and its effectiveness will be used to further improve the recommendation of future actions. The ability to act and deploy changes to building operations, within agreed boundaries and delegations, subject to human intervention and override.
- What we are hearing from our are 4 key areas where they want to focus their IOT activities, because that is where they see the most value. The first 3 are very tangible and quanitifiable, and they include Energy Management, Space Management and predictive maintenance. The 4th as described previously is not so easy to quantify, but it generally agreed that employee productivity IOT use cases are the key to ensuring the attraction and retention of the top class employees, whilst ensuring that they constantly, deliver output to the best of their ability. And we do this by using the same IOT data that delivers the operational efficiencies and making it available in a format that is useable and actionable for the employees. This involves using the WIOT platform to ensure that the disparate data sources are bought into one place where the data can be managed, analysed and acted upon in a secure platform. Organizations are at dfferent stages of their journey in the cognitive era. Early adopters have digitized processes, instrumented buildings and assets, and have learned from cognitive pilots to make operations more efficient, responsive and agile. They understand their end users’ customer experiences and are working on initiatives to improve satisfaction and loyalty. These early adopters are launching new services to capture new revenue streams and are making plans to enable and rebalance their workforce to make them ready for the cognitive era, equipping them with the needed solutions and tools. Accordingly, they have also thought through and are realigning delivery models and ecosystems.
- IBM Research developed an energy efficiency diagnoser as one example for a complex analytics. Energy becomes an important goal in building operation and is often required to target specific energy goals in daily operation. However, even for experts it is hard to predict the correct energy consumption as it depends on multiple influences such as the operation hours, occupancy and occupant behavior as well as external variables such as public holidays or the weather. This renders also common approaches such as rule systems or statistical outlier detection are not able to deal with this flexibility. Data-mining models on other hand consider these effect but provide only black-box models that are not interpretable by an operator, which is why he also does not trust the predictions.
- Energy wastage: The offering creates what is essentially an energy signature of each asset and compares it to the energy signature of each system. By doing this, IoT Building Insights identifies which assets are wasting energy. For instance, in the case of one client, some systems had a bad habit of switching to manual mode after an error. It was a very subtle glitch that was only able to be detected with the insights gained through IoT Building Insights. Visualize: For the first time, clients will be able to visualize the refrigeration systems, HVAC, lighting, IoT sensors, etc. as a whole. This offering stitches every system together and reporting on their vitals in real time. This means the client report and predict energy consumption for every system in every building. IoT Building Insights connects data from sensors, securely stores and archives it, creates a knowledge graph with that data and other data coming from external sources and building management systems, uses AI models to learn from the data, and presents visualizations of the data analysis and predictions from the AI models.
- Using “Uniform Metadata Schema” for buildings (called Brick), the knowledge graph understands large datasets of concepts and objects in a building and how they are related (drastically reducing need for human interaction). AI models are combined with the knowledge graph and learn from historical and sensor data, as well as information from building management systems, while normalizing it for weather, seasonality, time of day, occupancy, etc.
- This client was part of the prototype for IOT Building Insights By acquiring and harvesting the readily available data from one store and applying big data statistical approaches such as predictive analytics to a large data set, Tesco Ireland was able to validate refrigeration performance and identity case anomalies against standard policies and control strategies within its chain of grocery stores. This deeper knowledge not only yields significant energy reduction and savings in the short term but can also have a positive impact on future refrigeration control strategies. Real Business Results Tesco Ireland gained clear results demonstrating that optimizing the performance of its in-store freezers would help it save a significant portion of its total energy costs. Consequently, ensuring that its freezers operate at the right temperature helped the company cut refrigeration energy costs by up to 20 percent, which could realize savings of USD25 million a year across the wider UK and Ireland estate. Maintenance teams in the future should be able to assess the freezers remotely, diagnose any problems and arrive onsite with the right parts for repairs.
- KITT: Will benefit clients by connecting the dots between assets, values, location, measurements, etc. It helps them see the bigger picture of how all their individual systems and devices are connected, which in turn helps the client understand what things are driving the most energy spend. WEATHER: Other competitors in this space do not have out-of-the-box access to weather data. With this data the offering is able to give a client a precise picture of how weather was affecting energy consumption. Linking weather data to energy models allows us to predict how much energy will be used and which systems are struggling under those conditions, particularly on hot days when the systems have to work harder to keep up. More energy to keep things cool on hot days? Seems like a no brainer. But when you don’t have a solution capable of connecting the dots, even the obvious is hard to see from a store level, let alone enterprise level. AI models for predictive analytics: For many IBM clients faults in a system happens at annual audits. By using AI models, IoT Building Insights can spot indicators of potential failure in near real time. By spotting the tiniest anomolies in energy consumption patterns, clients will have insights at his or her finger tips, allowing them to be proactive before those tiny anomalies became bigger problems.