The Hidden Power Behind the Energy Performance of Commercial Buildings
Real-Time Data and Building-Specific Data: The Hidden Power Behind the Energy Performance of Commercial Buildings
Software-based rapid, non-intrusive and cost effective evaluation of building energy performance makes thousands of energy relevant data understandable in terms of energy efficiency opportunities.
These solutions typically combine whether, real-time energy consumption, occupancy and building specific data together and help us understand what is happening in the building with regards to energy usage. Beside this, they indicate which building parameter is affecting the energy consumption so that energy saving actions can be taken immediately.
What are Real-Time Energy Data and Building-Specific Data?
Real-Time Energy Data is formed by the measurements of metering devices throughout the day at regular intervals, usually in 15 minutes. However, these intervals can vary from one minute to one hour depending on the tuning of metering devices, namely, building control systems, energy analyzers or smart meters.
On the other hand, building-specific data is related to general characteristics of the building such as structural, mechanical, electrical, etc. These characteristics give us information regarding the physical properties, occupancy profile, air conditioning system and lighting controls.
Playing With the Data
Real-time interval data and building-specific data show the hidden pattern behind energy usage as if they take X-Ray of the building when they are evaluated with analytics engines. The parameters that affect the energy performance of a building mostly are the climate and operational parameters of the building energy systems. Software solutions with dynamic simulation capabilities can calculate energy performance of the building based on the building design and operational data. When the result of simulation is combined with the real-time data energy characteristic of the building get uncovered. From this point, it is possible to make recommendations about operational efficiency such as changing temperature set points within certain time periods or decreasing base loads levels during holidays.
Furthermore, some retrofitting suggestions can also be made, such as changing conventional light fixtures with LEDs, increasing insulation level of envelope or changing the window glass with lower U-value ones. The effects of all these suggestions and changes on the energy flow and energy cost can also be showed in building-specific.
The Value of Data for Demand Response
Power system deregulations and smart metering technology infrastructures have been built day by day. The improvements at electricity regulative framework and smart metering technology enable utilities to utilize demand response activities in order to significantly decrease the peak price and electricity price volatility under conditions of tight electricity supply.
Change in electricity demand of end-use customers from their typical daily load curves in response to varying price of electricity over time increases the reliability of the power grid. Moreover, since electrical generation and transmission systems are generally planned to meet the peak demand, any decrease in peak demand reduces the requirement of installing new power plants and transmission lines resulting in considerable savings.
Combining real-time data and building energy simulation output, demand response behaviors of commercial buildings can be modeled. Software capable of analyzing various demand response strategies at given real time electricity prices make building energy management more cost efficient. For example, changing room set points during peak demand hours can reduce HVAC costs. It is possible to determine the electricity demand of the building by running simple physics based energy simulations supported by machine learning algorithms. Scaling these models to a larger region for the similar types of buildings, district scale electricity price can also be predictively calculated. This is also very valuable information for electricity retailers. Therefore the dynamic response of the commercial building stock in a real-time pricing market is revealed.
Not a Fortune-Teller but Statistics Predicts the Future of Energy Usage
There are two fundamental energy consumption analysis approaches: top-down and bottom-up approach. Taking building design and operations into consideration, the bottom-up approach uses the real-time energy consumption data and building specific data to simulate the results of changes to building operations and building design. On the other hand, top-down approach is more related to statistical models.
Robust statistical models of commercial and industrial building energy use can be created as a function of outdoor air temperature, occupancy, energy generation and other independent variables.
One of them is the least-square regression method. This method provides a robust prediction of energy consumption by setting up mathematical relationships between historical energy trend and future weather forecasts. When evaluating buildings with this method, the importance of visualizing energy use data cannot be overstated. In general, our eyes are much better at identifying patterns and trends from graphical information than from tables of numbers.
Advanced Data Analytics with Provolta
Robust building energy performance analytics platforms, which are capable of providing valuable information like mentioned above, can reduce the time required to identify and evaluate energy efficiency measures.
When real-time energy data and building specific data is combined with building energy simulation engines and statistical models through sophisticated data analytics platforms, the rest is up to the software itself. It is possible to see energy efficiency opportunities related to building physics and operational schedules, to get retrofitting suggestions and to reduce the consumption according to the peak demand hours. From this point, Provolta, the building energy operating system developed by Reengen is a very good example of such an advanced data analytics platforms. Its unique bottom-up approach gives buildings the ability to understand and control their energy consumption as well as maintain their continuous energy efficiency. State of the art artificial intelligence algorithms interpret the structured and unstructured data gathered from different building components and convert it into actionable intelligence for building system controls. Provolta provides a low-cost, low-risk solution that dramatically expands the analytical capabilities of buildings in saving energy and utilities in managing demand in their service territories in real-time.
