Building Energy Performance Analytics Software and Services

Summary

Building Energy Performance Analytics Software and Services

Building Management Systems: Data Analytics Software and Services vs. Conventional

Software packages and services that analyze energy and performance data for fault diagnosis as well as optimizing system performance. They also establish a baseline and calculate savings based on the baseline.

Synopsis:

A recent NEEA study identified 51 Energy Management and Information Systems (EMSI) that store, analyze, and then display energy use for buildings or systems. Vendors predominately offer meter-level EMIS (using monthly or hourly data) with a cloud-based "software as a service" allowing suscribers to view and analyze energy data online.

Savings are dependent upon the type and quality of information provided to the user. Core features of EMIS systems include flagging of corrupt data, showing energy use profiles in a graphical format, comparison with historical energy use data, comparisons across portfolios of buildings, and energy cost and perhaps greenhouse gas emissions estimation. Systems that capture data on a shorter time scale may set up "dashboard" displays to summarize current, daily, or weekly energy consumption and operating status. Advanced systems may include load aggregation, end use benchmarking, and provide recommendations for efficiency opportunities. Savings depend upon how the building(s) was originally managed versus how operations change given the availability and use of the provided information. EMIS vendors claim energy savings ranging from 6 to 30%.

Some EMIS systems include diagnostics, alerts, and building optimization using advanced real-time analytics, fault diagnosis, and reports. These systems can access and use data generated by existing building DDC systems and possibly additional sensors, such as the commercial product DABO (Diagnostic Agent for Building Optimization) developed by NR Canada (www.ifcs-tech.com/en/product/daba/index.html).

Energy Savings: 10%

Energy Savings Rating: Limited Assessment What's this?

Level	Status	Description
1	Concept not validated	Claims of energy savings may not be credible due to lack of documentation or validation by unbiased experts.
2	Concept validated:	An unbiased expert has validated efficiency concepts through technical review and calculations based on engineering principles.
3	Limited assessment	An unbiased expert has measured technology characteristics and factors of energy use through one or more tests in typical applications with a clear baseline.
4	Extensive assessment	Additional testing in relevant applications and environments has increased knowledge of performance across a broad range of products, applications, and system conditions.
5	Comprehensive analysis	Results of lab and field tests have been used to develop methods for reliable prediction of performance across the range of intended applications.
6	Approved measure	Protocols for technology application are established and approved.

TAG Technical Score: 2.34

Details

Building Energy Performance Analytics Software and Services

Building Management Systems: Data Analytics Software and Services vs. Conventional

Item ID: 353

Sector: Commercial

Energy System: Multiple Energy Systems--Energy Management

Technical Advisory Group: 2011 Energy Management TAG (#4)
Average TAG Rating: 3.1 out of 5
TAG Ranking Date: 09/29/2011

Synopsis:

Baseline Example:

Baseline Description: Adjustments to HVAC Made During Scheduled Maintenance
Baseline Energy Use: 10.5 kWh per year per square foot

Comments:

This baseline assumes the efficiency is 10% lower than optimum due to components being out of calibration.

The 2009 Commercial Building Stock Assessment gives the actual electrical building energy use index (EUI) for various types of heating and cooling systems (Table D-EA5). Office buildings with electric heating and cooling have an EUI of 20.1 kWh/sf/year. Office buildings with no electric heating or cooling use only 8.2 kWh/sf/year, indicating that the combined HVAC heating and cooling energy use is 11.9 kWh/sf/year. (For all commercial buildings, the corresponding numbers are 19.9 and 9.4 kWh/sf/year, respectively for a heating and cooling use of 10.5 kWh/sf-year).

Since this technology can be applied to many types of commercial buildings, a baseline HVAC energy use of 10.5 kWh/sf-year is assumed (NEEA, 12/21/2009).

Manufacturer's Energy Savings Claims:

"Typical" Savings: 10%
Savings Range: From 6% to 30%

Comments:

Savings are dependent upon the type and quality of information collected by the EMIS and provided to the user. Core features of EMIS systems include flagging of corrupt data, showing energy use profiles in a graphical format, comparison with historical energy use data, comparisons across portfolios of buildings, and energy cost and perhaps greenhouse gas emissions estimation. Systems that capture data on a shorter time scale may set up "dashboard" displays to summarize current, daily, or weekly energy consumption and operating status. Advanced systems may include load aggregation, end use benchmarking, and provide recommendations for efficiency opportunities. These EMIS systems do not control equipment (like an energy management system or smart thermostat); instead they provide baseline information and feedback.. Savings depend upon how the building(s) was originally managed versus how "actionable" the information provided is i.e. how operations change given the availability and use of the provided information. EMIS vendors claim energy savings ranging from 6 to 30%.

Best Estimate of Energy Savings:

"Typical" Savings: 10%
Energy Savings Reliability: 3 - Limited Assessment

Comments:

Building operators or managers need to respond to identified problems. The magnitude of savings might not be apparent to the manager who weighs the savings versus the cost of the repair. The building owner needs to have the resources to respond to anomalies as they occur.

Energy savings for DCV and economizer alone is found to save about 30% for most occupancies and locations. 60% energy savings are noted in Oregon Energy Offices and NEEA, (Stipe, P.E., 2013). But, for the purpose of this ET, we assumed the fix would occur when the energy use for a component is out by 10%.

Savings can vary widely depending on existing building systems and maintenance programs, energy manager experience and knowledge, and how well the program is implemented. Many building system mechanical components are already designed with various levels of diagnostics, alarms, and alerts. This measure description really defines a strategy that results in time savings during commissioning and an earlier detection of faults or equipment operating outside of acceptable norms. A comprehensive strategy involves equipment selection and deployment, operator training,and subsequent trainings or rewards to ensure that the verified savingsare maintained (i.e. to establish persistence of potential savings).

Energy Use of Emerging Technology:

9.5 kWh per square foot per year What's this?

Energy Use of an Emerging Technology is based upon the following algorithm.

Baseline Energy Use - (Baseline Energy Use * Best Estimate of Energy Savings (either Typical savings OR the high range of savings.))

Technical Potential:
Units: square foot
Potential number of units replaced by this technology: 1,056,378,400
Comments:

To determine the maximum possible technical penetration of this technology, include most of the conditioned space of commercial buildings in the Northwest except for that with the simplest of HVAC systems, such as unit heater, unit heat/cool units, and baseboard units. Below is a table based on Figure 15 on p. 23 of the 2009 Commercial Building Stock Assessment (CBSA) with a breakdown of the different types of HVAC systems included here. From the table, 80% of the conditioned commercial floor space could apply this technology. The square footages are taken from the preliminary data set for the 2013 CBSA update, with estimated totals for 2014. As shown in the two tables, the total conditioned space of commercial buildings to which this technology could apply is 2,112,756,800 sf.

While this strategy can be employed at all commercial buildings, many of the larger commercial buildings, including schools and universities already have energy management systems. Even small offices are using programmable controllers with feedback and remote access features and even alarms for thermostats, plug loads, and other equipment. It is impossible to accurately predict savings from implementing this strategy due to the unknown and constantly varying baseline and energy savings double-counting can easily result as savings are most often associated with equipment deployment. The technological potential should be reduced by that percentage of building managers that already use sound building management and or continuous commissioning strategies. Given the lack of data, it is assumed that 50% of the commercial sector floor space currently employs best management practices.

Conditioned Commercial Floor Space

	Total Floor space	% Conditioned	Conditioned space
Source	(NEEA, 2014)	(NEEA, 2009, App C)
	3,118,000,000	84.7%	2,640,946,000

HVAC System	% of Conditioned Space	Area (s.f.)
Boiler/Chiller	16%	422,551,360
Duct Heat/Chiller	3%	79,228,380
Water Loop Ht. Pump	4%	105,637,840
Boiler Only	8%	211,275,680
Pkg. Ht./DX Cool	35%	924,331,100
Duct Heat/DX Cool	5%	132,047,300
Air-Air Heat Pump	6%	158,456,760
Pkg. Heat Only	3%	79,228,380
Total	80%	2,112,756,800

Source:

(NEEA, 2009, Pg 23)

Regional Technical Potential:
1.11 TWh per year
127 aMW
What's this?

Regional Technical Potential of an Emerging Technology is calculated as follows:

Baseline Energy Use * Estimate of Energy Savings (either Typical savings OR the high range of savings) * Technical Potential (potential number of units replaced by the Emerging Technology)

First Cost:

Installed first cost per: square foot

Comments:

There is no cost associated with the baseline practice. As for the cost of the packages described here, that is not well established yet. Furthermore, the costs of the software packages themselves are only part of the total cost. The entire effort of managing the software and acting on its recommendations (including checking out false positives) would have to be included in any rigorous cost-benefit calculation.

Automated diagnostic tools or techniques, also called Fault Detection and Diagnostics (FDD), are often used in conjunction with an existing energy management control system (EMCS). Using automated diagnostic tools during building commissioning or retro-commissioning can reduce the time required for problem detection and the associated costs as compared to manual diagnosis. Other benefits include improvement in the long-term persistence of energy savings and commissioning efforts, resulting in increased efficiency and performance of building systems.

Source: Natural Resources Canada:"Recommissioning (RCx) Guide for Building Owners andManagers"

Cost Effectiveness:

Simple payback, new construction (years): N/A

Simple payback, retrofit (years): N/A

What's this?

Cost Effectiveness is calculated using baseline energy use, best estimate of typical energy savings, and first cost. It does not account for factors such as impacts on O&M costs (which could be significant if product life is greatly extended) or savings of non-electric fuels such as natural gas. Actual overall cost effectiveness could be significantly different based on these other factors.

Comments:

Unknown. This technology has the potential to be highly cost-effective given its potential to replace or augment highly valued experienced human analysts with relatively inexpensive information technology, sensors, and software.

Detailed Description:

This measure consists of a wide variety of software packages and services. There may be over 50 distinct offerings, and they are referred to by a variety of names including: building analytics software, energy information systems, building dashboards, fault detection and diagnostics, ongoing commissioning, and smart building systems. All are designed to accept data from buildings and process that data to inform efforts to improve energy performance. At a minimum, all of these packages accept hourly (or finer resolution) utility billing data.

More sophisticated packages accept additional inputs, such as: data from direct digital control systems; sensor networks that may collect temperature, humidity, and power measurements; and weather data, both historical and projected. They employ a variety of analytical techniques, ranging from human interpretation of graphed data to intricate statistical modeling of building systems. These analyses are intended to produce recommendations to improve building operations. Typical recommendations include turning off specific equipment left on during unoccupied hours, raising chilled water temperature settings to reduce simultaneous heating and cooling, and timing the startup of HVAC equipment.

These software packages and services may be provided in the form of software installed on the building owner’s equipment, software provided as a service over the web, or as a consulting service in which the employees of the vendor operate the software and make recommendations. Energy savings claimed by vendors range from 6% to 30% of overall building energy consumption.

We break down this sector into four distinct offerings types, which are not mutually exclusive. Offerings are listed below in order of relative cost and relative expected energy savings:

Utility bill analysis. Hourly (or finer resolution) utility bill data and weather station data is accepted as input. Using regression analysis, that data is disaggregated into end-uses. Energy consumption is normalized and benchmarked against similar data from other buildings. The analytic system makes recommendations for specific operations improvements based on the benchmarking results. We are aware of only one vendor in this category: FirstFuel Software

Human analysis. Utility bill and other data sources are input. Such inputs may include end-use device power, temperature, humidity, and weather station data. Such data is tracked against a variety of performance metrics, and multiple data streams may be graphed against one another to highlight trends. Human technicians use software to spot trends, identify problems, and recommend solutions. Such software may be operated by onsite facility staff or by specialists trained and employed by the software vendor. Vendors (and product trade names) include: NorthWrite (Energy WorkSite), EnerNOC (PowerTrak), Noveda Technologies (EnergyFlow Monitor), and eSight Energy.

Automated comparison to rules and performance indices. Utility bill and other data sources are input, and the additional data sources described above apply here as well. The software compares processed data to a wide variety of rules and performance indices and makes recommendations based on these comparisons. Such software may be operated by onsite facility staff or by specialists trained and employed by the software vendor. Vendors (and product trade names) include: IFCS (DABO) and Facility Dynamics (Performance And Continuous Re-commissioning Analysis Tool).

Automated comparison to statistical models. Utility bills and other data sources are input. The software compares processed data to statistical models of building performance. Not only are recommendations made to remedy building dysfunctions, but the statistical models may also be used in a predictive manner to produce recommendations for near future operating parameters. Examples of vendors (and product trade names) include: IBM (Intelligent Building Management) and Building IQ.

Some diagnostic tools automate the process of collecting data, predicting energy use, detecting faults with physical systems and helping to diagnose their causes. They are typically installed on the front end of the building's EMCS to help the building operators not only monitor but also diagnose operations within their buildings. Automated diagnostic tools generally use the sensors from the EMCS to assess operational parameters such as air and water flow rates, temperatures and power measurements, to determine whether the system is working properly. If a problem is detected, the FDD tool then sends a report or alarm that notifies the building operator. A few of the tools quantify the energy waste related to specific problems, allowing prioritization of maintenance tasks.

Standard Practice:

As far as we know, the standard practice is to not use this technology at all. The use of automated building control systems is widespread, and there is not a line that separates where these control systems end and where the building energy performance analytical software and services begin. However, few building control systems incorporate the sorts of data analyses done by even the simplest analytical software packages.

Development Status:

Even though a few of these packages have been around for about a decade, they are still in the early market introduction phase. We have no information that suggests their status in the Pacific Northwest is any different than it is elsewhere.

Many Northwest utilities are conducting EMIS pilot projects, including(PECI, 2013):
---Avista: Using Resource Associates International system to view 5-minute resolution data at 65 buildings,
---BPA: Testing Catalyst, a rooftop HVAC controller at 43 sites,
---SnoPUD: Using Lucid Design EMIS at 10 Starbucks stores,
---Energy Trust of Oregon: Conducting a small and medium business pilot with Northwrite/Air Advice and Kite & Lighting,
---SnoPUD: Testing the Pulse Energy EMIS at a school district,
---NEEA: Testing the Northwrite EMIS at hospitals,
---PacifiCorp: Testing EMIS at industrial, light manufacturing, and commercial sites.

End User Drawbacks:

• Lack of independent cost and savings information. Some work has been done to evaluate the cost and benefits associated with implementing this technology, but virtually none of it has been made public. More work is needed to demonstrate the cost-effectiveness of the various offerings available and enable buyers to distinguish between offerings.

• Too many offerings in the market. With about 50 offerings available, customers are bound to be confused. Eventually, there will be a shakeout and the market will separate the winners from the losers.

• Different strokes for different folks. No one package can be optimal for everyone. Different people are bound to find some packages more useful than others. For example, some will prefer one way of organizing information and some another. Customers with more expertise in-house may prefer less expensive packages that enable them to do their own analysis. Less knowledgeable customers may prefer the more sophisticated packages that do more work for them.

• Savings depend on motivation and skill of users. Most packages output recommendations that must then be implemented by customers. Customers who lack the motivation or the skill to act on those recommendations will not benefit from the potential savings offered by those recommendations.

• Savings also depend on individual building characteristics, including size, types of systems on site, and the level to which those systems have been maintained before the building energy performance analytics package was implemented.

Operations and Maintenance Costs:

No information available.

Effective Life:

Comments:

The persistence of the building analytics software and services packages is unknown. The savings produced are likely to persist for the useful life of the software and services because the software is designed to flag any lapses in savings.

Competing Technologies:

The only competing technologies would be custom analyses done by experienced practitioners and building controls packages that incorporate some of the features above.

Reference and Citations:

Hannah Kramer (PECI), 10/09/2013. Inventory of Commercial Energy Management and Information Systems (EMIS) for M&V Applications
NEEA

Young Lee, 10/05/2010. Smarter Buildings with Analytics: A Framework of Building Energy Performance Analytics for Portfolio of Public Buildings
SimBuild 2010, 4th National Conference of IBPSA-USA

Hannah Friedman, 05/06/2011. The Building Performance Tracking Handbook: Continuous Improvement for Every Building
California Commissioning Collaborative (CCC)

Erik Greensfelder, 02/01/2011. Building Performance Tracking in Large Commercial Buildings: Tools and Strategies
California Commissioning Collaborative (CCC)

Joy Ulickey, 04/01/2011. Characterization of Fault Detection and Diagnostic (FDD) and Advanced Energy Information System (EIS) Tools
California Energy Commission (CCC)

Mark Effinger, 02/01/2011. Subtask 4.2 Research Report: Investigate Energy Performance Tracking Strategies in the Market
California Energy Commission (CCC)

Andri Kofmehl, 09/29/2011. Energy-Smart Buildings: Demonstrating how information technology can cut energy use and costs of real estate portfolios
Accenture

Kevin Kircher, 11/29/2010. Toward the Holy Grail of Perfect Information: Lessons Learned Implementing an Energy Information System in a Commercial Building
2011 ACEEE Summer Study on Energy Efficiency in Buildings

LBNL, 11/29/2010. Measurement-based Energy Management at a Low Energy Campus: Business as Usual and Exemplary Energy Management
Lawrence Berkeley National Laboratory

Jessica Granderson, 06/16/2010. Building energy information systems: user case study
Energy Efficiency

Jessica Granderson, 01/15/2010. Building Energy Information Systems: State of the Technology and User Case Studies
Ernest Orlando Lawrence Berkeley National Laboratory

Jessica Granderson, 07/13/2009. Preliminary Findings from an Analysis of Building Energy Information System Technologies
Ernest Orlando Lawrence Berkeley National Laboratory

Kyle Marini, 03/02/2011. Using Dashboard to Improve Energy and Comfort in Federal Buildings
Ernest Orlando Lawrence Berkeley National Laboratory

Rank & Scores

Building Energy Performance Analytics Software and Services

2011 Energy Management TAG (#4)

Technical Advisory Group: 2011 Energy Management TAG (#4)
TAG Ranking: 6 out of 59
Average TAG Rating: 3.1 out of 5
TAG Ranking Date: 09/29/2011
TAG Rating Commentary:

Technical Score Details

TAG Technical Score: 2.3 out of 5

How significant and reliable are the energy savings?
Energy Savings Score: 2.3 Comments:

Nov 2011 Comments:
1. Savings have the potential to be significant. Will vary widely depending on a variety of site factors.
2. High theoretical potential. Depends entirely on what the package offers, and how the owner/user responds to it. Either the customer is going to need a highly skilled building operator/manager, or the cost of electricity is going to need to go way up to make the savings real.
3. Savings are variable, and reliability is an open question. Savings reliability will probably need to be addressed in program design, both through measure life and continuous monitoring.
4. I would say for different tools this answer is all across the board. Some have better ways of quantifying change than others. Some will need to be paired with another M&V approach and many depend on the behavior technique that is paired with the software or service. Would be good to test key systems.
5. Can be good but dependent on analyst skills
6. Savings can be high, but may not be consistent across sites and vendors.

How great are the non-energy advantages for adopting this technology?
Non-Energy Benefits Score: 2.6
Comments:

Nov 2011 Comments:
1. Potential to manage comfort, indoor air quality, and water in addition to energy.
2. The questions are how much benefit will accrue from hour of customer effort, and how much occupancy discomfort may result from a serious push to save energy.
3. For certain situations, it seems like the benefits may be significant, but in generally I do not see large non-energy drivers here.
4. Insights into more than just energy. better building control and comfort.
5. These services also have a potential making building more demand responsive and be part of the smart grid.

How ready are product and provider to scale up for widespread use in the Pacific Northwest?
Technology Readiness Score: 2.4
Comments:

Nov 2011 Comments:
1. With so many vendors, seems like
2. There is a lot out there, and much of it probably works. But the options are difficult for the usual person to sort out and evaluate. They can definitely save analyst time when compared to old energy consumption data evaluation tools. But I feel that the new tools still require a very intelligent, knowledgeable person to sort out whether or not they are making sense. I might be wrong.
3. Wide range of products right now. There does not seem to be a core set of features to consolidate on. More powerful analytics, better user interfaces, and lower cost all seem to be advancing, but slowly.
4. Product are ready, but scaling to meet the market at cost the market will bear is lacking.

How easy is it to change to the proposed technology?
Ease of Adoption Score: 2.4
Comments:

Nov 2011 Comments:
1. The simpler packages are virtually plug and play. The most complex packages require lots of work integrating a variety of data and sensor networks. Average it out and you're somewhere in the middle.
2. Normalization of pre-post energy consumption comparisons remain an art as well as a science. Until there is some standardization and labeling of algorithms, quality control is going to be difficult for the user/utility, particularly if it's the fox guarding the hen house. It can still be a topic for dispute, and a combination of analytical intelligence, time and ethics.
The potential to include alarms to trigger problem fixing has high potential, but is also an art as well as a science. If the number of alarms is too high, and/or the staffing too low, or the person who sets up the alarms doesn't know what he/she is doing, it's a mess.
There's a potential to save huge amounts of energy be creating poor indoor air quality or thermal discomfort. I often find myself these days in environments with poor indoor air quality, probably due to the emphasis on energy efficiency and monitoring of performance. Unfortunately the potential to save energy by outside air economizers has lost popularity over the years, with chilled beams, VRF, etc."
3. It seems that the users will have to make some significant changes to have success with these products. To the extent that there are third party implementors, this could improve the ease of adoption.
4. Depends on the system

Considering all costs and all benefits, how good a purchase is this technology for the owner?
Value Score: 2.0
Comments:

Nov 2011 Comments:
1. Judging from case studies, users are getting lots of value. What it's costing them isn't clear yet. There seems to be lots of potential to get large savings at low cost.
2. I don't think this is going to work very well unless (1) the tools begin to conform to standard third-party tests and standard algorithm types, or (2) the cost of energy goes way up (maybe by a factor of 3).

If the cost of energy goes way up, and the customer is required to pay a significant portion of the service, the free market may take care of itself."
3. Hard to judge, because costs and savings are largely undefined.
4. Depends on how expensive the system is and what it provides. true cost effectiveness hasn't been proven in many instances since most test are still in pilot phase.
5. Without implementation of recommendations there is no value. Not all users will implement.

Completed:
1/20/2012 9:37:29 AM
Last Edited:
1/20/2012 9:37:29 AM