Understanding and Managing Multi-Site Commercial Building Performance and Energy Use with Monthly Utility Billing Data

Understanding and Managing Multi-Site Commercial Building Performance and Energy Use with Monthly Utility Billing Data

Dale T. Rossi

Field Diagnostics works with multi-site facility managers to assess energy use in their buildings.  A question that is commonly asked is “how can we know which of our facilities have an energy savings opportunity and for those that do, how big is the opportunity and what actionable suggestions can you give to achieve cost effective savings?”  The perceived problem is that getting that answer seems hard.

There are two reasons for this. One reason is that building management system (BMS/EMS) data is sometimes hard to access and when it is available the volume of the data may seem overwhelming and getting meaning from it seems complex.  The other reason is that collecting information about each facility with site audits or from field reports is expensive and it feels like that that kind of data isn’t always consistent and reliable because it comes from different people in different places at different times.

Field Diagnostics has developed an approach to providing answers to these question using easily available, objective and low cost/no cost data sources.  All that is required is the size to each facility, the information on the electric bill and weather data available from The National Oceanic and Atmospheric Administration (NOAA).  The answers that are derived from this include: Please Press "Read More"

• Is there an opportunity because of a high based load in the building? This means lighting, plug loads and other non-HVAC electrical loads.  In many commercial buildings this can be simplified to mostly lighting.
• Is there an opportunity because of low efficiency HVAC systems in the building? Meaning how much does the energy used in the building rise as the temperature and humidity outside rises.
• Is there an opportunity because the building is too leaky? Meaning is there too much outside air or too much solar radiation energy entering the building.  In many commercial buildings this means broken or miss-set economizers or manual outside air intake.

Using utility bill data to get these answers in multi-site facilities is inexpensive, it’s holistic and it’s fast. There even are times when this leads to uncovering an opportunity to make a simple change to an EMS set point that can have an impact on the entire portfolio.

Using utility bill or smart meter data for these calculations requires some processing. 

Step one – Get available data. Building size and Monthly utility bill data. This includes electric energy use, peak demand and cost and gas use and cost for 1-3 years into the past aggregately assumed to reflect current operations. Limit the amount of history appropriately.

Step two – Calendarization. Calendarize monthly electric and gas usage to join billed data (e.g. different meters and/or providers) from different time periods into a standard time frame.

Step three – Monthly and annual time frames. Focus on electric energy use first. Heating (common and important use of gas) is generally a less import cost driver. Electric Peak demand and impact of utility rates come later too.  Look at total average annual electric energy use (kwh/year) to consider the impact of weather from all seasons. Get average annual usage even if there are incomplete years of data (e.g. 2.5 years) by averaging energy use for each month and then summing all months.

Step four – Normalization. Normalization compensates for factors that impact energy use and cannot be changed. The following factors may be considered, building use (e.g. restaurant, retail, or office), climate or weather, building size represented by floor area in sq. ft., hour and daily schedule differences, building activity level (e.g. sales volume).

Normalization can be accomplished by either dividing by a normalization factor (e.g. sq. ft., cooling degree days, and sales volume) or by grouping sites with similar values (e.g. building uses, weekly or monthly data, and ASHRAE or CA climate zones) and only comparing them to each other.  It assumed that the monthly data is normalized for hour and daily schedules that are similar for each month. To help with this, monthly data is divided by the number of days in each period to get average usage per day. Weekly data, usually summarized from 15-minute interval meter data, is better for schedule normalization when available.

Step five – The Empirical Model  – Deeper insights into energy savings use can be derived from understanding the average daily outdoor temperature dependence of monthly or weekly energy use.  Below is an example of average daily energy use for a facility with non-electric (e.g. gas) heat. 

Figure 1  an example of average daily energy use for a facility with non-electric (e.g. gas) heat.

The empirical model transforms normalized data into three parameters, base load, balance point and the cooling slope.

• When its cooler outside, usage is flat at the “base” value.  Base usage includes all loads that are not outdoor temperature dependent.
• Mechanical cooling turns on at the “balance point” temperature, where energy use transitions from flat to sloping up.
• The “cooling slope” quantifies how the HVAC equipment runs more, using more energy as it gets warmer outside.

Recommended Actions – To manage energy use consider the following actions:

• Order sites by sq.ft. normalized base usage.  Compare sites with high base energy use. Assuming no mechanical cooling is running in the base energy use, factors impacting it include lights, plug loads or other electric loads as well as scheduled HVAC fans for ventilation.
• Order sites by balance point temperature.  Low values may be energy savings opportunities – mechanical cooling is running when it’s cool outside.  Factors impacting it include lights, plug loads and/or other internal heat loads as well as no or failed economizers – closed when needed.
• Order sites by cooling slope.  High values may be energy savings opportunities, meaning more cooling energy needed for the same increase in outdoor temperature. Factors impacting it are either associated with inefficiency cooling or leaking buildings and include low mechanical cooling efficiency, excessive ventilation air and poor building insulation or open windows/doors

Step six – Simple Building Model

The simple building model transforms the three empirical model parameters into two additional parameters, cooling efficiency and conductance, or how well the inside conditions are insulated from the outside conditions.  These two additional parameters are more decoupled in how they relate to physical characteristics of the building and are interesting because they can be affected by energy management tasks, meaning each parameter is related to distinct and separate actions that can be taken.

Recommended Actions

• Order site by conductance.  High values may be energy savings opportunities.  Factors impacting it are building envelope or ventilation issues that occur independent of call for mechanical cooling including. These include:
• Supply fan related ventilation related to ON fan (run independent of mechanical cooling)
• Open doors or windows
• Poor building insulation

• Order sites by cooling Efficiency.  Low values may be energy savings opportunities.   Factors impacting it include:
• Mechanical cooling efficiency
• Ventilation effects that only come on when mechanical cooling is active (e.g. supply fan related ventilation related to AUTO fan)

Problem solving approaches

Problem solving involves using the available analytical and other tools to execute a consistent, methodical, scalable and cost effective process to identify and prioritize energy cost savings opportunities.

Field Diagnostic Services is here to help.

For more information about using Monthly utility billing data to better understanding and manage multi-site commercial building performance and energy use please contact:

Todd M. Rossi, Ph.D.

President, Field Diagnostics

609/240-3656

rossi@fielddiagnostics.com