How on Earth Could Big Data Analytics and HVAC Systems Fit in the Same Picture?
In 2014, globally 8.4 zettabytes of digital content was created. The advance in Internet of Things and digitization of equipment has increased the volume of data points available from the field. Besides the volume, complexity of data and establishing interrelationships is another huge challenge. Over the last few years, it is not just the volume of data that has increased exponentially; it is also the variety — we capture more variables today on top of capturing more samples of each variable. Taking the example of buildings — the traditional approach to energy-baseline definition would have been to correlate whole building consumption with outside air temperature, maybe also occupancy and schedule of operations. However, with advanced sub-metering and building automation systems, we now can analyze data by individual service type, specific building location and even specific time of the day. We, therefore, have more variables to take into account (and of course the large volume of data) for the same analysis — which is great as it allows more targeted analysis, but which also means that we need to identify the most important variables for analysis, as otherwise we could easily be trapped into misleading analytics.
Upcoming Big Data Disruption in Building Energy Management
Today, technology allows us to capture and utilize big data to connect various elements of the ecosystem such as facility infrastructure data, performance and operational data of the systems installed in these connected facilities, maintenance histories and manufacturers’ data. Advancements in technology are making it easier to connect, capture and use Big Data. Technology finally has evolved in a way that allows us to digitize building subsystems and make them transportable virtually as big data. Therefore, the challenge of Big Data Analytics really starts with big data management — and then the analytics. Big Data Analytics must in turn lead to “big insights” and “big actions” — the challenge today is in completing the “Big Data and Analytics Value Chain.”
Reengen completed this value chain with “Continuous Energy Commissioning Service (CECS)” running on top of Provolta Energy OS. This service helps building operators to understand what is going on with the HVAC systems in real time, the implications of that and what kind of actions are recommended to improve building performance and take the automated optimization actions.
The Pillars of optımum Energy Commissioning Service
As CECS helps building operators moving from raw data to analytics, they will have the capability to move from more descriptive analytics to predictive analytics. In addition to building owners and operators, ESCOs and utilities can leverage the proposed CECS in many ways to create tangible value. The benefits of CECS are as follows:
Advanced analytics: CECS can help with better understanding of building and equipment performance. Identifies, classifies, and quantifies building energy consumption deviations from design intent or an optimum with dynamic energy simulations over cloud, support classification and identification of root causes of such deviations. It allows historical trending, pattern recognition and correlation between cause and effect of issues and events occurring in the various building and HVAC subsystems.
Actionable insights: CECS enables benchmarking of a building’s Lighting, Plug Loads, HVAC system performance against industry standards or benchmarks. Optimizes the microgrid environment agents such as building integrated renewables, electric vehicles and storages. Provides real time alerts and predictive recommendations to the building operator and automated optimizations to the existing Building Management Systems for corrective actions and fine tunings.
Predictive maintenance: Through proper analytics on past performance data and issue trends, future potential maintenance issues are identified through simulation and predictive technology. Such actions help extend equipment life, reduce operating costs and minimize disruption.
Informed decisions: Leveraging the cloud-based energy modelling programs and big data analytics engine, building energy managers can model their future energy requirements and simulate their future operating budgets. CECS provides a decision support mechanism and an interface platform between Building Managements Systems and Energy Utility platforms for utilization of demand response and energy efficiency services.
Connected communities: At a fundamental level, virtualization of building subsystems allows harnessing dispersed experts by creation of a connected community of advisors to enhance performance of buildings. CECS makes the “Bringing the building to Virtual Energy to the Buildings” possible through cloud based dynamic energy simulations, organized big data analytics and the Internet of Things.
Originally published at medium.com on January 25, 2017.