Automated Continuous Commissioning After application of continuous commissioning, technicians can focus on problems with the highest energy savings potential or probability of comfort violation.

Jan Široký Research Team Leader Energocentrum Plus

Introduction

Nowadays heating, ventilation and air conditioning (HVAC) systems do produce enormous amount of data. These data are stored in building management systems (BMS) ready for exploitation. However, in practice, these data are not always utilized. The operator or local technician usually does not have enough time to analyze all data. But what would take hours to human operator can be done in seconds by a computer. The problem is how computer can analyze data similarly to a skilled technician. During the recent year, there was a shift from a simple alarm notification to more advanced algorithms. One can find different names for these emerging tools (e.g. energy intelligence software, operational diagnostics, monitoring-based commissioning). In this article we will use a term continuous commissioning. The main goal of the article is to present results of application of continuous commissioning in practice.

We will describe application of continuous commissioning to a customer in the European Union. The customer has 150 mostly office buildings connected to cloud based SCADA system Mervis ( https://mervis.info ). More than 10 000 000 unique value records (e.g. temperature, valve setpoint, energy consumption,...) are stored in database each day. Apart from common SCADA functions (schemas, alarms, trends, messaging …) Mervis offers analytical tools for everyday use. But all these bells and whistles are almost useless once you have to work with tens of thousands variables and any regular evaluation and analysis of the historical data becomes a nightmare. That is a moment when the advanced analytical methods of continuous commissioning proves the most valuable.

Before application of continuous commissioning, technicians were dealing with the most urgent HVAC problems. Usually without consideration of severity of possible impact. After application of continuous commissioning, technicians can focus on problems with the highest energy savings potential or probability of comfort violation. Thanks to application of continuous commissioning significant energy savings were achieved.

Technical solution will be described in the next chapter, followed by the brief description of the used methods. Selected examples and results summary are presented in the last section.

Technical solution

At the core of the continuous commissioning process described in this article is Mervis - cloud service for complex supervision of building and other technologies. Its foremost function is to integrate different control and building management systems that have been installed in above mentioned more than 150 buildings over the last 20 years.

Technologies by Siemens, Johnson Controls, Daikin, Landys&Gyr, Schneider, Buderus, Honeywell, LG, Saia, Sauter and many others were integrated. The measured data are securely communicated over the internet.

The basic rule is that all communicated variables (tags, data points) are logged into the highly optimized database every three minutes but the system allows for any sampling period starting from less than one second. Even the least important variable can play significant role in later data analysis.

Once the data are collected and available online - the facility managers can do their regular work of monitoring the building technologies.  More importantly they receive a complex report derived from the historical data every week. The report highlights the most important violations of comfort, energy efficient regimes etc. but allows for deeper analysis of the data directly in Mervis cloud system using direct links to particular measured values stored in the cloud. Methods that are used for data analysis are briefly introduced in the next section.

Methods used

We are focused on methods that can be simply applied without time consuming configuration and tuning. Except of data tagging, no special data preparation is needed. All preprocessing, analysis and avoidable cost calculation is done automatically, without any user input. Thanks to this fact reports can be generated regularly on daily, weekly or monthly basis. The goal is to process raw HVAC data and prepare summary information for an expert that can validate the results and perform an action if needed.

The following analysis can be performed:

• Energy consumption with respect to building regime
• Energy consumption with respect to weather conditions
  
• Indoor air quality
• Heating system setback
• Domestic hot water with respect to building regime
• Air handling units operation
• General detection of non-standard profile of measured values
  

Each analysis uses different algorithms, from simple rules (air handling units) and basic statistical characteristics to sophisticated methods such as neural networks (detection of non-standard profiles) and mathematical modeling (heating system setback). Example of setback analysis is depicted in the figure below.

Crucial part of the system is estimation of avoidable cost. If possible, impact on energy consumption is estimated. The goal is to be able to compare detected incidents in terms of avoidable cost, not to provide a rigorous calculation of avoidable cost for each particular incident.

Example of a heating system setback analysis. The chart depicts an output of dynamical model simulation. The model is based on a real building data. The green-yellow surface indicates time needed for heating a building to 22 °C for different outdoor temperatures and setback temperatures. For example, it will take more than 11 hours to heat up the building from 20 to 22 °C when outside temperature is -10 °C.

Case study - results

As mentioned above, the described approach was applied to a building portfolio of a customer in the European Union. The portfolio consists of office buildings with a predefined regime (opening hours) and similar usage patterns. Detailed knowledge of building regime (imported from computer aided facility management systems) was crucial for the analysis.

Electrical energy consumption outside office hours was identified as the lowest hanging fruit. Complex evaluation of electrical energy consumption with respect to building regime was performed. Ten buildings with the highest saving potential were selected for a detailed on-site investigation. The most common problem was wrong setup of air handling unit time program. This was fixed by a simple reconfiguration of building management system (no investment needed). Outcome of such reconfiguration is depicted in the figure bellow. The second common problem was lighting. In some cases lighting was turned on outside office hours without any reason. In one case, we found an oversized UPS. The UPS not removed after major reduction of IT in the building. The overall savings achieved on ten selected buildings are $ 20,000 per year with total investment lower than $ 4,000.

Carpet chart of electrical energy usage. Weekly regime can be recognized with significant reduction of weekend electrical energy consumption after air-handling-unit regime adjustment.

This summer we had focused on unnecessary mechanical cooling outside office hours. Again, we had selected ten worst performing buildings. On-site investigation confirmed that the reason of suspicious electrical energy consumption is cooling outside office hours. Integration of autonomous cooling units into a monitoring and control system was needed. Required payback three years was not satisfied in most cases due to installation costs.

Application of continuous commissioning provided significant benefits to a customer. We had performed evaluation of energy consumption of the buildings before and after application of continuous commissioning. In total, there was 9 % reduction of energy use after application of continuous commissioning. Major advantage of continuous commissioning is in long-term cooperation. It ensures that energy savings actions are not ruined due to an inappropriate action (e.g. switch to manual regime by a local technician). From this perspective, the customer is saving several hundred thousand dollars each year.

About the Author

Jan Široký is leader of research team of Energocentrum Plus company. Jan has gained his Ph.D. at the University of West Bohemia, Department of Cybernetics.  During his studies he undertook an internship at ETH Zurich. The main activity of Energocentrum is operation and servicing of energy equipment, technological equipment of buildings and measurement and regulation. Energocentrum has its own research and development department. This team develops software products for measurement and control (e.g. cloud based SCADA system Mervis - https://mervis.info ). Research team is focused on advanced control and monitoring algorithms for buildings.