Divyanshu Kamboj, ERS, for Zondits
The introduction of Internet of Things (IoT) capabilities in the energy efficiency sector is making a very compelling case to reinvent the current practices of evaluation, measurement, and verification (EM&V) of energy efficiency programs. The traditional “snapshot” oriented approach comparing before and after energy usage scenarios is no longer sufficient for evaluating the success of energy efficiency projects or for identifying greater improvement opportunities in program planning, delivery, and implementation. The entire cadre of stakeholders in this industry – administrators, implementers, and evaluators, realizes the value of more accurate, granular, immediate, and continuous insights into the effectiveness of energy efficiency programs. A new approach to E M&V is emerging and, as discussed in previous articles and presentations, EDGE M&V embraces the latest sensor technologies and the IoT to make the sought-after changes possible. This paper explores the IoT side of the ongoing discussion.
[bctt tweet=”The IoT has enabled investigation of trends of facility occupancy and associated energy usage.” username=”ZonditsEE”]
What is the IoT? Where is it now?
At its very core, IoT simply means development of network connectivity between “things” in order to send and receive information. However, this concept of connectivity in the building’s environment is not particularly new, and the definition of IoT in this context has evolved multiple times over the last few decades. What we today call IoT is arguably an advanced rehash of the original term “controls,” which has deep roots in the building automation (BMS/EMS) world. Over the years, as technology advancements triggered innovation, the term controls evolved into more sophisticated “machine-to-machine” (M2M) communications. And with technology pushing the boundaries of connectivity between “things” today, IoT has emerged as the de facto vernacular to justify the promise to make buildings more intelligent and ubiquitously connected.
[mks_pullquote align=”left” width=”300″ size=”18″ bg_color=”#ffffff” txt_color=”#8224e3″]A dense nexus of sensor data collection points inside a building that help complete a high-resolution digital avatar of the monitored facility.[/mks_pullquote]Looking beyond the IoT hype, perhaps the most exciting and promising aspect is that the term “things” in IoT can now be used to represent virtually anything. This is due to the availability of low-cost sensors, robust wireless communication standards, and nimble data infrastructure backbones that have rendered previously dumb devices as smart, and environments previously unmonitored as now digitally accessible. This translates into a dense nexus of sensor data collection points inside a building that help complete a high-resolution digital avatar of the monitored facility, giving all stakeholders a granular look inside a building’s energy usage and occupancy patterns, operational efficiencies, and even the social dynamics. From an energy consumption perspective, this level of insight has been somewhat elusive in the recent past, both in terms of variety and granularity of sensor measurements and the cost-effectiveness of conventional remote monitoring solutions.
As IoT connectivity inside buildings continues to become more pervasive, it is resulting in an avalanche of data that needs to be properly contextualized and then used to deliver analytical insights in real time. Sensor measurements are streaming in from all corners of the building – whether it is varying light levels, temperature distribution across floors and rooms, CO2 levels in occupied areas, energy data from electric panels and HVAC systems, and much more. Add to the mix the outside temperatures to normalize the energy usage patterns, social media feedback on comfort levels, and possibly hundreds of different metadata input feeds, and we can then contextualize this sensor data flood in accordance with targeted analytical objectives. With real-time data as the key by-product of IoT, making sense of this ever-growing data lake is like putting together a massive jigsaw puzzle.
Coincidentally, few key developments have transpired to make the case for IoT in the building’s environment more compelling than ever before:
- IoT Standards and Communication Protocols
The proliferation of smart sensor devices from different manufacturers, running on various operating systems and using multiple communication networks, requires cross-platform data recognition and resource sharing. The development of such standards and their at-scale adoption is imperative for an interoperable device nexus, not just for emerging IoT devices in consumer and industrial space, but also for legacy equipment already in operation within buildings. Efforts such as AllJoyn/AllSeen Alliance, Industrial Internet Consortium, Open Interconnect Consortium, and Thread, among others, are well under way to implement this vision for an interoperable ecosystem of device connectivity and services.
[mks_pullquote align=”right” width=”300″ size=”18″ bg_color=”#ffffff” txt_color=”#8224e3″]The development of such standards and their at-scale adoption is imperative for an interoperable device nexus, not just for emerging IoT devices in consumer and industrial space, but also for legacy equipment already in operation within buildings.[/mks_pullquote]As new and existing sensor devices begin to receive their digital identities, communication protocols both wired and wireless have also matured in parallel to support a robust connectivity model for the assets in the field. This can now be accomplished at a fraction of a cost compared to the previous protocol generations, which posed limitations in wireless data transmission range and sensor nodes’ battery requirements – adding substantial operational overhead for provisioning and maintenance. Well established protocols such as ZigBee and Bluetooth had emerged from IEEE 802.15.X and have enabled much of mesh sensor topology applications. But these are now challenged with more recently emerging protocols in narrow band cellular and sub-GHz frequency bands such LoRa and SigFox, which provide longer connectivity range inside RF-unfriendly building environments, while meeting aforementioned requirements of low cost and low power device operation.
- Data Silos’ Integration
[mks_pullquote align=”left” width=”300″ size=”18″ bg_color=”#ffffff” txt_color=”#8224e3″]As IoT standards continue to gain traction, it is replicating the web-scale connectivity of “things” where software running on the devices is becoming application neutral and information generated is no longer residing on an island.[/mks_pullquote]As IoT standards continue to gain traction, it is replicating the web-scale connectivity of “things” where software running on the devices is becoming application neutral and information generated is no longer residing on an island. This vision to make every device 100% reachable from a common platform is selectively exposing the underlying data layers across device ecosystems. Although the security concerns of such interoperability could not be overstated, this horizontal integration of data silos holds tremendous value when we have fifteen different manufacturers of HVAC equipment in a single facility, with limited discoverable interfaces to exchange information. Prior to any complex data analysis activity, the key challenge is to combine and cleanse these data silos from different proprietary subsystems and emerging IoT devices under one master data infrastructure. For example, since data generated from a stand-alone BMS/EMS does not typically capture all of the operational trends of a building, the data streams from distributed IoT devices could be used to further enrich the analysis and optimize BMS operation. Such horizontal integration of data silos exposes previously hidden energy consumption patterns and helps grab those high-hanging fruits of a building’s operational efficiency improvements.
- Data Dictionary Standardization
In order to add context to the stories the buildings are trying to tell us, the surge of data streams from disparate systems, HVAC equipment, smart devices, and IoT sensor nodes requires clear relationship definitions between all such sources, as applicable. Since the purpose of data silos’ integration is to better understand the interactive effects of multiple systems within a network, a well-defined standard nomenclature is required to resolve inconsistent naming conventions and terminologies across multiple systems – to create one version of the truth. Ongoing efforts such as Building Energy Data Exchange Specification (DOE BEDES) and Project Haystack (open source collaborative initiative) are scripting a standardized common set of terms, definitions, and nomenclature to exchange information about building characteristics, efficiency measures, and energy use. Adoption of a universal semantic data model and meta-data tagging methodology will accelerate the interoperability of public and private software tools, which often come across data formats’ incompatibility and limited data descriptors while consuming data for analysis.
- Big Data Democratization
Data collection from IoT sensor devices and other systems, although it is a critical enabling step, requires advanced processing and cleansing techniques to convert raw data into actionable information. This value creation stage requires a framework that can handle data ingestion of varying volume, variety, and velocity. For example, to analyze a single data stream such as monthly billing information from a facility, an engineer can easily identify changes in monthly energy consumption with a dozen data points using conventional spreadsheet-based analysis. However, if the number and types of data streams now include 1-minute interval data from twenty different IoT devices, with each continuously providing ten unique sensor measurements, across a university campus of thirty buildings, the scale, complexity, and desired latency of data analysis very quickly gets unwieldy with conventional tools. However, the advent of advanced analytical platforms in the Big Data world has allowed democratization of data analysis regardless of the scale, type, and speed of investigation desired from such masses of data generated by IoT.
One caveat to grasp before adopting some of these Big Data technologies is that “one-size-fits-all” approach has its limitations, as a single data handling tool is designed for specific data type, scale, and storage and analysis objectives. That is why prior to selecting a Big Data solution it is recommended to perform a comprehensive requirements gathering exercise and use case categories’ definition to closely understand the analytical end points, associated data manipulations, and work flows. A complete end-to-end Big Data analytics platform will consist of integrated set of tools and technologies to solve specific analysis problems at hand – ranging from data ingestion, storage, processing, statistical and algorithm-based mining, and results visualization.
Relevance to EDGE M&V
[mks_pullquote align=”left” width=”300″ size=”18″ bg_color=”#ffffff” txt_color=”#8224e3″]Such dynamic and near real-time verification of energy savings will boost investor and market confidence in further promoting energy efficiency as a resource and a tradeable commodity.[/mks_pullquote]The ability to persistently measure energy savings as they happen, with desired granularity across the energy efficiency effort hierarchy, Portfolio > Programs > Projects > Measures, can now be accomplished with ubiquitous availability of energy data streams and occupancy patterns from remote IoT devices inside buildings. The quest to cost-effectively reach beyond the building meter for data at the subsystem and equipment level is no longer elusive. Furthermore, as IoT adoption continues to fuel this massive-scale data harvesting, it will enable identification of common macro-trends across portfolios, and distinct micro-trends within buildings and systems. Such dynamic and near real-time verification of energy savings will boost investor and market confidence in further promoting energy efficiency as a resource and a tradeable commodity.
Just like the people living inside them, buildings are effectively complex living organisms with systems and occupancy patterns that are continuously in flux. The wave of IoT adoption has enabled effective investigation of hyper-localized trends of facility occupancy and associated energy usage, with the use of more granular data and advanced analytical toolsets. In effect, the abundance of granular information derived from the IoT paradigm, overlaid with data and analytical frameworks in accordance with EDGE M&V, offer an opportunity for enhanced evaluations with deeper insights into causes and effects of program and measure successes and challenges.
Please continue to visit Zondits over the coming weeks and months as we explore the many aspects of EDGE M&V, and examine specific project examples that have taken advantage of these latest approaches.