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SMART BUILDINGS AS BUILDING BLOCKS OF A SMART CITY

Research Institute Electrical & Computer Engg

October, 2016 Virginia Tech Research Center

1 SMART BUILDINGS AS BUILDING BLOCKS OF A SMART CITY Professor Saifur Rahman Virginia Tech Advanced Research Institute Electrical & Computer Engg Department University of Sarajevo Bosnia, 06 October, 2016 Virginia Tech Research Center Arlington, Virginia, USA PPT slides will be available at

2 Research Focus Areas Advanced self-learning algorithms for intelligent buildings Interconnected communications solutions Energy harvesting and wireless sensor technologies Relationships among thermal comfort, acoustic comfort, lighting and indoor air quality Short and Long-Term Objectives Short-term: develop efficient hardware and software solutions for a small cluster of smart buildings Long-term: To provide a holistic early solution to the smart city problem that will make this cluster competitive for sustained external funding 2

Smart Buildings A smart building connects the building automation system with building operations, such as HVAC, lighting, water supply, sensor network and fire emergency.

To Grow 30% by 2020, http://www.iotsolutionprovider.com/smartbuilding/smart-building-market-to-grow-30-by-2020, December 2015.

3 Smart Buildings A smart building connects the building automation system with building operations, such as HVAC, lighting, water supply, sensor network and fire emergency. Implementing smart building solutions can provide: up to 30% savings of water usage up to 40% savings of energy usage reduce building maintenance costs by percent Source: Smart Building Market To Grow 30% by 2020, December Smart Cities Smart cities address urban challenges such as pollution, energy efficiency, security, parking, traffic, transportation, and others by utilizing advanced technologies in data gathering and communications interconnectivity via the Internet. Source: Internet of Things Philippines Inc., December

4 Key Areas for Smart Cities Systems are connected together by ICT to transmit and process data in the smart city concept. 7 From Smart Buildings to Smart Cities 8 Smart City Energy Smart Grid Smart Buildings Supported by ICT and distributed networks of intelligent sensors, data centers/clouds Smart city: Complex system of interconnected infrastructures and services Energy: Smart electric power grids, smart gas networks, smart water systems Smart grid: Bi-directional flows of energy, remote control/automation of power, integrated distributed energy Smart buildings: Intelligent building automation systems, smart devices, productive users, grid integration 4

Smart Building & IoT Evolution 1980 1990 2000 2010 2020< Building Control HVAC control Lighting control Building Automation Building management Building control Building Performance Energy management

92 billions 50 billions Virginia Tech (VT) Solution For Smart Buildings BEMOSS is a Building Energy Management Open Source Software (BEMOSS) solution that is engineered to improve sensing and control

5 Smart Building & IoT Evolution < Building Control HVAC control Lighting control Building Automation Building management Building control Building Performance Energy management Remote monitoring Remote control Smart Building Intelligent buildings Green buildings Grid integration Internet of Thing (IoT) Connected Devices 0.2 billions 6.92 billions 50 billions Virginia Tech (VT) Solution For Smart Buildings BEMOSS is a Building Energy Management Open Source Software (BEMOSS) solution that is engineered to improve sensing and control of equipment in small- and medium-sized commercial buildings. BEMOSS BEMOSS monitoring and control: Three major loads in buildings HVAC Lighting loads Plug loads BEMOSS value: Improves energy efficiency and facilitates demand response implementation in buildings. 10 5

square feet) or medium-sized (between 5,000 sqft and 50,000

These buildings typically do not use Building Automation

6 Why BEMOSS? Buildings consume over 40% of the total energy consumption in the U.S. Over 90% of the buildings in the U.S. are either small-sized (<5,000 square feet) or medium-sized (between 5,000 sqft and 50,000 sqft). These buildings typically do not use Building Automation Systems (BAS) to monitor and control their building systems from a central location BEMOSS Interoperability Communication Technologies q Ethernet (IEEE 802.3) q Serial Interface (RS-485) q ZigBee (IEEE ) q WiFi (IEEE ) RS-485 Data Exchange Protocols q BACnet (IP and MS/TP) q Modbus (RTU and TCP) q Web (e.g., XML, JSON, RSS/Atom) q ZigBee API WEB q Smart Energy (SE) q OpenADR (Open Automated Demand Response) Smart Energy Profile (SEP) 12 6

BEMOSS Software Architecture Layer 1 User interface layer Layer 2

layer Layer 4 Connectivity layer Scheduling Tampering detection Web UI

Monitoring agents VOLTTRON TM - Information Exchange Bus (IEB) Network

User Management Alarm/Notific ations Application Control agents API

Demand response OpenADR agent Platform agent API Translator for WeMo

BEMOSS discovery agent, we know: The device is present in the building.

Sensors/ BEMOSS automatically discovers new load controllers deployed in

(WiFi) (BACnet/M odbus) CT80 (ZigBee SE) ICM (WiFi) Nest (WiFi) HVAC Load

BEMOSS Core Lighting load controller Light switch (WiFi) (BACnet)

7 BEMOSS Software Architecture Layer 1 User interface layer Layer 2 Application & data management layer Layer 3 Operating system and agent layer Layer 4 Connectivity layer Scheduling Tampering detection Web UI Device Discovery agent User Interface Operating System and Agent Monitoring agents VOLTTRON TM - Information Exchange Bus (IEB) Network agent API Translator for RadioThem. User Management Alarm/Notific ations Application Control agents API Translator for Wattstopper Load management API Translator Mobile UI Demand response OpenADR agent Platform agent API Translator for WeMo Metadata Database (PostgreSQL) Time-Series Database (Cassandra) Cloud sources (e.g. OpenADR, server, weather services) 13 BEMOSS Plug & Play With BEMOSS discovery agent, we know: The device is present in the building. Device model number, e.g., 3M-50. What the device can do, e.g., monitor temperature and adjust set point. Sensors/ BEMOSS automatically discovers new load controllers deployed in a building PowerMeters CT30 Power meter (WiFi) (Modbus) Power CT50 meter (WiFi) (BACnet/M odbus) CT80 (ZigBee SE) ICM (WiFi) Nest (WiFi) HVAC Load Controllers 14 VAV controller (Modbus) RTU (Modbus) Philips Hue (WiFi) BEMOSS Core Lighting load controller Light switch (WiFi) (BACnet) Lighting Load Controllers Step-dimmed ballast (ZigBee) Smart plug (WiFi) Light sensor (BACnet) Occupancy sensor (BACnet) Plug load controller (BACnet) Smart plug (ZigBee) Plug Load Controllers 14 7

8 10/2/16 BEMOSS on Embedded Devices XU4 CPU: Arm Cortex RAM: 2 GB Price: $138 Size: 5.8 x5.6 CPU: Arm Cortex RAM: 2 GB Price: $74 Size: 3.3 x2.3 CPU: Arm Cortex A9 Quad 1 GHz RAM: 1 GB Price: $129 Size: 3.4 x2.4 This enables low-cost deployment, and expandability. 15 BEMOSS: Solutions for Small Buildings BEMOSS Core HVAC Controllers Plug Load Controllers ZigBee mesh 16 ZigBee mesh Lighting Load Controllers 16 8

9 BEMOSS Scalability: Solutions for Larger Buildings BEMOSS Core Floor 2 Floor 1 BEMOSS Zone 2 BEMOSS Zone 1 17 BEMOSS Dependability BEMOSS Core Floor 2 Floor 1 X BEMOSS Zone 2 BEMOSS Zone

10 BEMOSS Redundancy BEMOSS Core Floor 2 Floor 1 BEMOSS Zone 1 19 Key BEMOSS Features BEMOSS #1 #2 #3 #4 #5 #6 Open source & open architecture Plug & play Interoperability Cost effectiveness Scalability & reliability Security 20 10

11 Living Laboratory 1021 Prince St., Alexandria, VA Area: 25,000 SF Energy: MWh/mo. Peak load: 61 kw 21 Classroom Monitoring by BEMOSS Power meter Environmental sensor (CO2, noise, temperature) BEMOSS core Thermostat Motion sensor Plug load controller 22 11

Remote Access Each BEMOSS node is connected to the Internet to allow

23 BEMOSS Potential Applications Integration of machine learning

Integration of algorithms to manage a large amount of data collected

12 Remote Access Each BEMOSS node is connected to the Internet to allow remote access. 23 BEMOSS Potential Applications Integration of machine learning algorithms to get better understanding of power consumption in buildings Integration of algorithms to manage a large amount of data collected from load controllers/sensors Integration of algorithms to allow management of multiple buildings in a transaction-based energy network

Novel Communications Solutions Key enabler for smart cities: Internet of Everything (IoE) Millions of inter-connected devices and sensors Design, deployment and management of this IoE requires

13 Novel Communications Solutions Key enabler for smart cities: Internet of Everything (IoE) Millions of inter-connected devices and sensors Design, deployment and management of this IoE requires fundamentally new communications solutions Two representative challenges to be addressed: Massive access management: Number of devices and sensors in an IoE will be orders of magnitude higher than the number of human devices in current networks Incorporating self-powered sensors: Not easy to replace batteries in millions of sensors. Deployment of self-powered sensors is an attractive solution. Requires fundamentally new solutions to handle uncertainty in the availability of energy at these sensors 25 Massive Access Management in IoE Data generated by IoE characterized by small data payloads and large number of wireless devices Current LTE cellular systems not efficient for small payloads due to huge overhead (at least 3 stages of transmission) Key idea: send small data payloads as a part of the random access request (to reduce transmission stages) Representative contributions from Dr. Dhillon s group: Throughput optimal random access strategy Characterization of regimes in which this single-stage random access strategy is information theoretically optimal 26 13

14 Thank You Professor Saifur Rahman Virginia Tech Advanced Research Institute Virginia, USA ( 14

Virginia Tech Research Center

Virginia Tech Research Center 12/4/15 Building Energy Management Open-Source Software (BEMOSS) HVAC Controllers Lighting circuit(s) Lighting Controllers Plug load Controllers Presentation to IEEE PES NoVA/DC chapter June 24, 2015 Saifur