At the end of July, a report from the Association for the Conservation of Energy (ACE) found that over a third of non-domestic buildings in London have the worst energy ratings under Energy Performance Certificate (EPC) standards.
London is not alone and is in good company with other global cities in its failure to address chronically bad efficiency standards in buildings.
BuildingIQ is well-established in assisting urban planners in this area and it released its new cloud-based platform, Predictive Energy Optimization (PEO), also in July.
Decentralized Energy spoke to Steve Nguyen, senior director of marketing at BuildingIQ about how his firm’s technology is incrementally improving energy efficiency in the cities in which it operates.
Decentralized Energy (DE): Does the platform benefit buildings through the use of combined heat and power, onsite power, onsite solar, or district energy?
Steve Nguyen (SN): BuildingIQ is all about using data to optimize energy usage. Therefore, from the perspective of our newly launched 5i platform, it’s somewhat irrelevant as to where the energy comes from – grid, local generation (solar, community solar, on-site generation) or storage.
To us, clean energy sources such as solar and cogeneration are simply more data streams to be incorporated into our optimization algorithms. While we are in the early stages of working with customers to incorporate local power sources, our platform and optimization engine is capable of providing signals that could help sophisticated buildings determine when, and from which source, they should get power.
A key element that’s already incorporated into our platform is utility tariff structures and demand ceilings. The hourly cost of electricity is a huge factor in determining the appropriate energy source at the appropriate time. At this point, we greatly anticipate continuing our work with customers to hone this particular part of the technology as those sources increase in popularity and their data streams become more accurate and consistent in the near future.
DE: Are there any particular projects ongoing or completed that make use of BuildingIQ and the above systems?
SN: BuildingIQ’s Predictive Energy Advisory Services are being utilized at the Women’s and Children’s Hospital Precinct in Adelaide, South Australia. This is an approximately 1.1 million sq. ft. precinct with a complex central plant. The plant encompasses a combination of a CHP-driven trigeneration plant and the electric chiller based plant.
The trigeneration plant is used to generate electricity, chilled water and hot water, 80 percent of which gets used for heating, ventilating and air conditioning (HVAC) purposes followed by domestic purposes. The electric Chillers are utilized for the HVAC purposes only. The plant is powered by gas and is utilized for providing the base HVAC Systems with necessary chilled and hot water. The electric chillers come on top to work in accordance with the capacity.
BuildingIQ’s Predictive Advisor uses a mathematical model, generated through the analysis of internal data (occupancy comfort, building characteristics and meter data) and external data (demand response signals, energy tariffs and weather forecasts), to understand the relevance of the trigen generated electric power along with the utility generated grid power.
The platform then provides a power prediction forecast for the next 24 hours from 4pm onwards every day. This Power Prediction gets utilized by the building team to see what aspects of the grid-utilized HVAC Plant can be tweaked to manage power use and demand. This strategy is mainly used to observe the potential KWH draw that can be used, based on the weather forecast, followed by the ability to see what was predicted over the last 3 days vs. the actual KWH used by the building, with the goal of dropping the peak demand for the facility on hotter days.
DE: What sort of obstacles have been overcome to achieve energy efficiency in cities as a result of this platform and what obstacles remain?
SN: There are many city-wide efforts around the world designed to achieve carbon or energy reductions. For instance, we have an ongoing project with the Washington D.C. Department of General Services that is designed to limit resource consumption, reduce environmental impacts, lower costs, extend the life of capital assets, optimize operations, and increase occupant engagement and education. A lot of the obstacles come down to the actual state of the Building Management System (BMS) itself.
While predictive control is absolutely beneficial in terms of energy savings, operational efficiency, and tenant comfort, getting the BMS to the point where it can respond to the intelligent system is an entirely different breed of animal. We’ve had many instances where our team arrives on site only to find out that the BMS that the owner thought they had performs NOTHING like the BMS that’s actually in place. For instance, it’s common to have systems with inoperable thermostats, defective heating coils, stuck paddles in the VAVs, and so on.
What this means is that the first step is often a retro-commissioning project that is not only unexpected, but could take months. While necessary, such projects slow the overall roll-out and impact actual expenditures. At the actual building level, this is perhaps the biggest obstacle.
DE: Do you think the technology can be rolled out globally? I’m thinking of Europe which is identifying serious inefficiencies in how it heats and powers its buildings and cities generally.
SN: Yes. Our technology is BMS agnostic and open ended in terms of the data streams that it can ingest. Often solutions from BMS vendors will only solve/impact their own systems, but the reality is that cities and portfolio holders have to deal with massive complexity in their holdings due to the individual nature of buildings.
Solutions like those provided by BuildingIQ sit atop any BMS – from rudimentary to state-of-the-art – to deliver on the goals that are typically set forth by regulatory agencies.