From Data to Insight — Evolution of Energy Intelligence
In order to establish a great sustainability platform in your facility, you need to use an energy intelligence platform that finds the problems you can’t see manually. Nowadays sub-meters, sensors and energy analyzers provide rich data stream about your critical equipment & real time energy performance. If you have a platform with advanced analytical libraries, artificial intelligence modules constantly scan your facility data, learn how to find the hidden problems in your building, you do not need to do manual calculations or lost in excel sheets; automated rules perform them for you.
Many buildings can access their monthly utility bill data, but monthly raw data alone is not enough to find many of the energy savings opportunities hiding there. For aggressive savings in energy and operational costs, there is a clear need of real time energy intelligence solution. Real-time notifications, detailed analytics, executive & summary reports, mobile apps, public displays — Reengen Energy IoT Platform delivers insights in any format. With a legacy BMS or without, it helps you focus on the problem spots in your facility or plant.
Reengen Energy IoT Platform has some competitive advantage over other tools in the market. We can consider it the last chain of the energy intelligence evolution. The critical components of this evolution can be stated as follows :
Adaptive tariff optimization & different rates
Not all kilowatts are created equal. Utilities vary prices by time of day and season to encourage buildings to reduce energy use during peak grid load. Time-of-use rates are designed to reward ratepayers who can shift electricity to off-peak periods. Because rates are a zero sum game, those rewards are underwritten by ratepayers who can’t, or don’t shift their use.
Grid peaks are expensive for utilities, who have to provision sufficient generation capacity to handle maximum grid load. To reflect these costs, utilities are charging time-of-use rates that vary with time of day, day of week, and season of the year. With rate and bill data, you can see the actual operating cost impact of a particular sub system of the building, isolated from the billing period length, weather, and utility rate factors also driving bill variance.
Weather normalizations
Weather is one of the dominant drivers of building energy use, and every building responds to outside conditions in its own way. Seemingly random fluctuations caused by the interplay between air temperature, humidity, cloud cover and other factors mask other. Weather normalization uses a statistical model to filter out the signal from the noise, the contribution of weather conditions to building energy use. Once the weather component has been characterized mathematically, it can be subtracted out of the daily load curve, yielding a “weather-normalized” picture of building energy use that more clearly shows underlying trends. With weather data, you can answer the question “What portion of my building’s energy use is due to weather, and what portion is due to other factors?”
Automated baseline calculations
The only constant in buildings is that they change. Occupancy changes, equipment upgrade cycles pass, special events occur, and even space use fluctuates. How can you access accurate performance feedback with all of these changes? The secret is a learning baseline model that adapts alongside your building, accounts for the changes, and simultaneously learns the new normal.
Automated diagnostics
There is yet a further distinction between the difference between how the building is using energy compared to how it typically uses energy (for that time of day, day of the week, and weather) and how, and when, the building is drawing power. Fault detection diagnostics require two things: looking very carefully at the load curve and applying sophisticated mathematical models to it. A simple example is holiday use, although that’s easy enough to spot on a raw load curve. A tougher fault to spot with the naked eye is a subtle bump in base load–though given that buildings use more energy throughout the year when shutdown vs. in use. With analytics applications, you can easily identify hard-to-spot drifts in building startup and shutdown times.
Dynamic forecasts
Perhaps the most space age differentiator between the value of smart meter data and the value of analytics is in forecasting future energy demand. Since peak demand charges can drive 40% of your building’s summer utility costs, ignoring peak demand management is equivalent to ignoring about half of your bill. With accurate and precise forecasts, you will know when your building is likely to set its monthly peak demand charge, allowing you to pay attention to that half of your bill while ignoring the days that don’t matter.
