You cannot manage what you cannot measure.
Energy Analytics helps you manage your energy use by providing visibility into your energy consumption.
We at Kamal Cogent Energy, work with a number of energy analytics service firms based in the United States of America helping them provide automated or semi-automated energy analytics services. Our services could be of various types and depends on the needs but essentially revolve around

• data collection or data processing
• visual inspection of energy/demand data
• running routines or scripts to better visualize the data
• developing automated routines via machine learning or R scripts
• trend data fault analysis
• developing energy models to calculate savings
• developing reports in various formats.

Tools and Visualization
We provide these services on offline tools like spreadsheets, statistical packages (like R or tableau), developing codes (in Visual Basic, Pearl) or customized solutions. We also provide these services directly through client portals where real time data is visible for a building or set of buildings. Such data could be energy, demand, other commodity (gas, CHW, HW, steam), or cost ($).

Analytics Activities

1. Data Processing – energy analytics starts with utility or usage data and such data frequently needs to be formatted or processed into useful formats. Our cost effective resources make this possible whether it is data from utility portals, your customized portal or provided spreadsheet files/CSV files. We can digest large data sets in statistical packages if they cannot be handled by MS Excel.
2. Visual Inspection – the data needs to be visually inspected for which it needs to be graphed/plotted many different ways. We have skilled engineers who inspect thousands of buildings every month and are very adept at finding faults. Visual Inspection also leads us to big picture faults, which we then focus on through “deep dives”.
3. Running Scripts for Visualization – We use custom scripts in VBA or R to better visualize multi-dimensional data sets, look at statistical co-relation such as R-square, standard deviation, SSD etc values to find faults and thus resulting in energy optimization. We can look at electric data (energy, demand) and commodity data (steam, CHW, HW). We use tools like Tableau and others for this.
4. Automated routines and machine learning – We develop (i.e. write code) automated routines based on machine learning which can digest the data, see the pattern of the fault, number of repetitions and “auto learn” to catch that fault in the future and correct the same.
5. Trend Data fault analysis – We are very adept at looking at BMS trend data which could be thousands of IO points (temperature, damper, speeds, valve position, offsets, resets, start-stop and others). We use tools like Universal Translator (by PGE) or Tableau and others for this. This is commonly called automated fault detection or fault diagnostics.
6. Energy Savings Calculations – we have literally completed thousands of energy models to quantify the savings from the faults discovered in the data. These models could be simple excel type calculations or more complex 2D or 3D models in tools such as eQuest or Ecotect or others. They could be EEM specific or whole building in nature. The models could be rigorous and meet IPMVP or ASHRAE norms or more high level depending on your needs.
7. Reporting – We take pride in providing crisp and to the point reports. The same can be in narrative form, tableau reports, power point slides, mark-ups in existing reports or others.