Start Tracking Natural Gas Consumption in Real Time

It’s now possible to track natural gas consumption in real time, and here’s why you should. Knowing your consumption in real time makes it possible to associate consumption data with specific internal and external factors. These associations enable insights and actions into making improvements in key performance indicators (KPIs) like energy efficiency, conservation, and sustainability.

Looking at energy consumption in real time is not new. Electrical power consumption has long been available in real time. That data is used by customers for behind-the-meter consumption analysis and benchmarking, by electrical utilities to determine demand rates, and for demand response. In the case of real time natural gas consumption data, knowledge of the data by the customer has even broader and more powerful uses.

Using Real Time Natural Gas Consumption Data

Real time consumption tracking is useful for benchmarking and consumption analysis. For example, matching up the rate of natural gas consumption in real time with indoor and outdoor temperatures, time of day, and other factors is useful for making comparisons with similar buildings. Comparing the performance of “Retrofit A” vs. “Retrofit B” with real time data is a powerful way to maximize financial outcomes and system optimizations.

Building dynamics can be determined and used to reduce wasted energy. If the data show that consumption causes indoor temperatures to overshoot their targets (e.g. on sunny mornings), a case can be made for installing a proactive energy control system. A similar analysis can be used for cooling applications that use natural gas fired chillers.

For larger portfolios of buildings, benchmarking the KPIs of similar buildings, then applying an energy conservation technology to one of the buildings will quickly demonstrate how much energy can be saved with that conservation technology. From there, ROI calculations can be made to determine if that conservation measure should be applied to the other buildings.

Faster, More Accurate Insights

Having consumption data available in real time yields faster and more accurate insights. The problem with the commonly used method of analyzing monthly billing data is that there’s a lot of useful information that’s missing because it’s impossible to extract it from the data. Billing data may include degree heating days and/or degree cooling days to match up with the consumption data, but that level of granularity has limited usefulness. First of all, the data that adds up to degree days provides very little potential for drawing actionable insights. The data doesn’t tell you what temperatures were and when (e.g. nights, weekends, etc) and what other factors were present, such as sunlight, wind, and occupancy level. Whatever insights that can be derived from the billing data might take a whole heating season to compile, and even then the conclusions might still be missing the mark.

Real time data can be used to separate out or factor in variables. In some cases it’s even possible to turn on and off energy saving features, adjust heating curves, or the like. Doing so can help to quantify the level of impact from the new feature or setting.

Real Time Energy Use Intensity (EUI)

For the purpose of illustration, here’s a simplified example of an hour by hour comparison of energy use intensity (EUI) on a scale of 1-10 over the course of 24 hours. Lower average EUI with the same external factors yields lower energy costs and lower emissions.

The chart shows average hourly EUI dropping from 5.3 to 4.3 over two “identical” 24 periods. Lowering EUI by approximately 10%, as in this example, is a substantial gain in efficiency. In reality, data can be obtained in even shorter time increments for an even greater level of granularity. As the data would be reported in therms (or similar), it becomes easier to calculate costs and savings.

Natural Gas Consumption, Carbon Emissions and Carbon Tax

In addition to saving money through reduced consumption, building owners, managers, and REIT investors also gain knowledge and insight regarding the quantity of CO2 emissions that are eliminated by lowering gas consumption. Lowering CO2 emissions has value, but the dollar value is difficult to quantify today. However, wherever a price is put on carbon emissions, those calculations become straightforward and readily available.

Next Steps

Contact CIMI Energy to learn more about tracking your natural gas consumption in real time.

CIMI Energy uses Energy Star Portfolio Manager and other tools to turn your real time data into action .

March 19, 2019September 3, 2020

Time to Make Energy Systems Proactive

Making energy systems proactive is a logical step forward in improving building energy efficiency

Proactive building energy systems can increase energy efficiency, improve comfort, lower energy costs, reduce peak energy loads, and increase property values.

What is a proactive building energy system?

A proactive building energy system uses forward-looking input factors such as a building’s thermal momentum, near-term weather forecasts*, and demand costs to help determine system outputs. Artificial intelligence (AI) software uses strings of data inputs and self-learning to calculate outputs and optimize system performance.

A proactive energy system can be an add-on part of a very basic system, or it can can be integrated with a complex building management system (BMS) using open protocols or something else.

Contrast with today’s reactive energy systems

Nearly all building energy systems in North America today are reactive. A typical example would be a temperature set-point being reached which triggers a relay that starts or stops one or more devices (e.g. pump, boiler, chiller). There may be a variable component to the output, such as a distribution temperature selected from a heating curve. Commonly, a PID control determines the targeted output . The production and distribution technologies themselves (e.g. condensing boilers, variable speed heat pumps, low-temp hydronic distribution, etc.) may be high efficiency in a stand-alone sense. Nevertheless, they are limited by the narrow range of input data that are available to them.

Key differences between reactive and proactive system control

When external factors are changing quickly as is typical with nearly constant changes in weather factors (such as temperature), or when demand rates kick in, the reactive system loses its ability to optimize energy inputs.

Proactive energy systems on the other hand, use a range of data that includes forward-looking data that feed dynamic algorithms. With proactive systems, there can also be some automated learning involved as improvements build on past improvements. This is where AI can play an important role. The AI is used to determine thermal inertia and thermal momentum, both of which are important for maintaining targets without over or under performance. Reactive controls don’t have this ability.

Fortunately, there are more similarities than differences. Proactive systems and reactive systems use the same building assets. So from an upgrade perspective, it can be very easy to make the change.

Examples of proactivity in our lives

Although you may not have thought about it in this way, being proactive is a natural part of our everyday lives. There are countless examples of being proactive that illustrate this point.

Here are a just a few examples from one thing many of us do every day: drive a car. Imagine you’re behind the wheel of your car and you’re driving down the road. You see a sign that says “Stop Ahead”. This is an opportunity to be proactive. You can take your foot off the accelerator and let the momentum of the car carry you forward to the stop line. That’s proactive and saves a little bit of energy. Now imagine you’re on a highway and you see a sign that says “Exit Right 1 Mile”. If that’s your exit, you can look for a good opportunity to move over to the right lane. That’s also being proactive. A third example is when you drive with your high beams on at night, you’re gaining more “visual data”; you can be more proactive in dealing with upcoming road hazards.

Of course, being proactive doesn’t necessarily have to lead to greater efficiency. Proactive measures in energy are worth doing if they help achieve an objective like lower energy use, lower demand charges, or higher comfort.

Thermal momentum quantified and used proactively

Being proactive helps increase efficiency in cases where momentum (including thermal momentum) and inertia are significant factors. Thermal momentum is identified and used by AI for proactive control.

As momentum is the product of mass and velocity, higher mass leads to higher momentum. As an example of this concept, heavy trucks benefit more from that “Stop Ahead” sign than does a car, and the car benefits more than a bicycle. A pedestrian may not benefit at all. So the greater the mass, the greater the momentum, and this is true in buildings as well. Proactive AI control can account for momentum and inertia, and use it to improve overall efficiency.

A multifamily building is full of walls, floors, ceilings, carpeting, furniture, and plumbing. Therefore it is relatively dense. An empty airplane hangar, a big space filled with nothing but air, is not dense. As a result, changes in thermal energy can occur much more quickly in the hangar (assuming the building size, energy systems, and building envelopes are equivalent). Because the multifamily building is denser, it takes longer to heat up, and is slower to cool down. All that mass inside the building is a heat sink. That mass is absorbing and radiating back out thermal energy constantly. If a heating system reacts to a set point being reached, and stops pumping heat into the building, that building mass will continue to radiate heat back into the living space. If the outdoor temperature rises or sunlight streams in, the interior of the building can become overheated.

Consistent building temperatures with proactive control

Another thing we can say about the multifamily building is that steady temperature is important to the people that live there. Zeroing in on a set point temperature is a challenge that is easier with AI and proactive control. Multifamily building owners know that the alternative to consistent indoor temperatures is either complaints, open windows in winter, or both. It also can lead to higher energy costs, increased tenant turnover, and lower rents.

Lowering demand peaks in district energy with proactive control

In cases where there is an energy demand rate**, it’s possible for a proactive energy system to optimize operation to pre-load heating or cooling, and to hold back from the high demand peaks that drive demand charges. As noted earlier, this is a valuable tool where building mass can be used to advantage. This is most common for buildings connected to district energy networks, but is evident in other energy scenarios as well.

Other applications for proactive energy control

Besides thermal energy, as described above, there are other forms of energy consumption that can, and sometimes do benefit from proactive control.

Natural gas consumption
Electrical power (demand response)
District energy (heating or cooling)
Chilled water /sensible cooling

*useful weather data may include the timing of upcoming temperature, sunlight, wind, and precipitation.

**energy demand rate – charged by some district energy utilities and electric utilities. Demand charges may be tiered, and can vary by time of day and time of year. In some cases demand rates change daily, with a day or less of advance notice. Demand charges help utilities pay for the higher marginal cost of supplemental energy sources, or the cost of infrastructure needed to meet peak demand.

March 6, 2019September 3, 2020

Proactive heating/cooling systems

The Point of Being Proactive

Proactive means forward-looking. Humans are naturally forward looking. We plan ahead; make budgets, plan our days, plan our vacations, plan for retirement, and so on. We do this because we know that the alternative to being proactive, which is being reactive, leads to less desirable outcomes (e.g. “Oh, today is my vacation, I think I’ll go to the airport and fly somewhere warm!”).

Most of us learn this by the time we are in high school. We’re able to be proactive because we’re capable of thinking ahead, and there’s usually some data we can use to help us plan ahead. We look at movie schedules, flyers for school plays, announcements of sports events and the like. All that foreknowledge is “data” that we can use to plan our lives.

We also use weather forecasts for some personal energy-related planning.

We bring along a hat and coat if we’re anticipating wintry weather.
We close our windows if we’re expecting rain or cold.
We close the window shades if we’re expecting a lot of sunlight on a summer day

So the point here is that being proactive is natural, depends on data, and usually leads to more desirable outcomes than is the usually the case for the alternative.

Please read the post titled “Time to Make Energy Systems Proactive” for a closer look.