While the definition of energy performance is concerned with measurable results related to energy efficiency, use and consumption, energy consumption is the component that organizations ultimately strive to reduce as it results in decreased energy needs and lower cost. To analyze energy use and consumption data, you must identify or develop methods that are effective and best fit your organization's needs.
How to do it
There are two tasks associated with analyzing energy consumption and costs:
2.4.1 Determine data analysis method(s) and assign responsibilities
Your organization can analyze and track energy in many different ways, from simple “homegrown” spreadsheets to very sophisticated and expensive software and web-enabled applications designed for large, multi-facility complexes. A variety of software packages are available in the commercial marketplace. The eGuide provides you with the DOE Energy Footprint tool that provides for basic energy data analysis for your building or facility.
Following is a general discussion of some of the typical methods that help with developing energy performance conclusions.
The methods of data analysis you use will depend on several factors:
Data availability
Desired output
Level of available competency for data analysis
Audience
Considerations and examples for each of these factors are below.
Data availability – Step 2.2.3 discusses the data needs for your energy management system (EnMS), much of which relates to the energy review, energy performance indicators (EnPIs), baselines, and energy objectives and targets. The scope of data collection can be considerable and the ability to collect this data is generally dependent on metering availability (see Step 2.3). Your organization’s facilities, equipment, systems and processes can have many potential sources for data collection e.g. pressure, temperature, flow, etc. However, if the metering is not available it may not be possible for you to collect the data for determining and evaluating the desired performance metric until additional metering is installed. For example:
If a boiler is separately metered, direct consumption data may be collected for energy performance evaluation. If facility metering is the only available metering, boiler performance may have to be evaluated using a portable flue gas analyzer or other measurement methods.
Desired output – What is the desired output you want to achieve from the analysis? Prior to determining the method of analysis to be used, you should clearly understand the goal of the data analysis. The Overview for this step mentions several uses for the data but you may also want to:
Determine performance level
Monitor operations
Evaluate against a benchmark or like equipment or systems
Evaluate the result of maintenance or improvement activities
Validate the impact of relevant variables
Consider the output you desire when you select a method for analyzing data. For example:
Significant energy uses can be simply determined (see Step 2.5.3) by placing your identified energy uses (see Step 2.2.2) in a list in order by consumption and selecting the one or two top consumers. On the other hand, if you want to determine the energy performance of one of the significant energy uses, it could require multiple data points and calculations with reference to performance tables. Similarly, if you want to validate the impact of relevant variables on a significant energy use, it may be necessary to conduct a statistical analysis.
Level of available competency for data analysis – Personnel who are performing the analysis must have a level of competency that enables them to conduct the analysis and evaluate the results. Personnel experienced in data analysis may be able to use a range of methods to analyze data. Less experienced personnel may only be able to conduct a very simple analysis using simple tools. Or if a more detailed analysis is required, personnel may need very sophisticated tools that require little manipulation or interpretation. Your organization must possess or obtain the level of competency necessary to conduct the desired data analysis.
Audience – Who is the audience for the analysis? Management will typically be more interested in financial data and effect of the EnMS on the bottom line. Operators will be interested in ensuring their equipment is operating at peak performance. Maintenance personnel will use performance as an indicator of the need for routine maintenance or repairs. Engineering personnel will want to verify that improvements made to a process are achieving the expected results. As an example:
Assume an improvement project is implemented in your organization. The Engineering department will want to perform calculations to verify that the estimated 10,000 MMBtu of natural gas was actually saved. Management will want calculations to verify the initial estimate of $40,000 cost savings and that the life cycle cost and return on investment will be realized.
Many simple analysis methods can be very effective in analyzing data collected in the energy review and providing the desired results. Some of these are discussed below.
Trend analysis – In a trend analysis you try to identify a pattern in the data. If a pattern can be established, any change in the pattern can be an indication of changes in energy performance that may require investigation if an unexpected pattern develops. For example:
Generally, the electricity consumption of an organization with natural gas heat will decrease during the winter because of the absence of air conditioning loads. A smaller decrease or the absence of a decrease could indicate equipment being left on or in need of repair (or an extremely warm winter).
Benchmarking – Benchmarking is the process of comparing energy performance data against a standard. Benchmarking allows a comparison to make a determination about current energy performance. Some of these standards can include:
Industry standards
Theoretical calculations
Similar equipment
Similar processes
Sister organizations
Competitor organizations
Previously established performance levels
Graphs – Graphs are a method of data presentation that generally allow much easier evaluation than large quantities of numbers on a page or in a table. Graphs allow for easier detection of data that is not fitting the pattern in a trend analysis or is out of line with a benchmark. Graphs can be very helpful in identifying anomalies or significant deviations which ISO 50001 requires you investigate (see Step 4.2). A few types of graphs include:
Line graphs
Bar graphs
Pie charts
Scatterplot
Time series graph
Ranking – Ranking is an ordering of items to establish a relationship. Ranking is a typical method for ordering equipment, systems and/or processes to determine significant energy uses (see Step 2.5.3). Ranking is useful for establishing the relative relationship among items so the appropriate criteria for focus can be applied.
Pareto analysis – A Pareto analysis is a form of ranking that can help identify areas of focus. Also known as the 80/20 rule it says that 80% of the effects come from 20% of the causes. With respect to energy consumption, 80% of the energy supplied will generally be associated with 20% of the equipment, processes or systems. While the actual ratio can vary significantly from the 80/20 ratio within a facility or organization, a large part of energy consumption can generally be attributed to a small number of equipment, processes or systems. A Pareto Analysis is helpful when evaluating projects or determining significant energy uses since it can serve to focus resources.
Energy balance – An energy balance is discussed in Step 2.5.2 and can help with accounting for energy consumption.
Heat balance – A heat balance is similar to an energy balance but typically is focused on one piece of equipment or system and involves balancing the amount of heat entering and leaving the equipment or system. For example:
The amount of heat entering a dryer via a gas burner must equal the total of the amount of heat that is absorbed by the dried product, emitted through the walls of the dryer and escaping through the dryer stack. A change in the stack temperature could indicate a change in energy performance (air circulation problems, incorrect dryer temperature, change in product flow, change in product moisture or temperature, etc. that allows more or less heat to exit via the stack).
Utility analysis – Analyzing utility bills is important to understanding how energy entering your organization is measured, how the cost is determined and allows you to monitor the bill for changes in consumption and cost. Monitoring the bill for each of your organization's energy sources allows you to identify errors, compare your energy performance with other organizations and review the results of improvement projects. Identifying errors is particularly effective if your organization has submeters that collect consumption data and/or conducts a regular energy balance to verify the amount of energy received from each source.
Financial analysis – One of the chief reasons for improving energy performance is to reduce costs. Keeping track of costs and evaluating the effect on the bottom line is a key function of management and they require data necessary to conduct their evaluation. Data needs can be related to general operating costs or the need for evaluating organizational changes. Management generally wants a financial analysis either before a change to evaluate how to proceed, or after a change to evaluate the impact and how it affects the organization’s economics. Sometimes they want both. Many financial methods and tools are available to you for conducting the necessary financial analysis.
Regression analysis– This is a statistical analysis for understanding the relationship between a dependent variable and one or more independent variables. The dependent variable depends on the independent variable(s) and will change in response to a change in the independent variable(s). Regression analysis can be used to predict consumption, which is a dependent variable, based on independent variables such as production, weather, occupancy or operating hours. Linear regression is the simplest and most frequently used, but there are many other types of regression models. An example:
Natural gas consumption for a building heating system, a dependent variable, will increase when the temperature, an independent variable, decreases and will decrease when the temperature increases.
Consider talking to other organizations that have conducted data analyses similar to the one(s) in which you are interested. Lessons learned by others can provide valuable information in identifying or developing analysis methods. They can discuss how well their method has worked, issues they addressed and provide insights that will help you effectively analyze your energy consumption.
Finding an effective method of data analysis is important for identifying energy opportunities that lead to cost savings. It will show areas that are significant and deserve the most attention. It can also help identify billing errors and hidden costs within utility rate structures. It will help your management representative communicate the value of energy management to top management and get the resources needed to make the energy management system successful.
Two case study examples of energy tracking are provided to show the variation in energy tracking methods. The first case study for a large enterprise illustrates the use of several tables and graphs to perform basic analysis. The analysis includes several different accounts for a given utility, converting the different energy sources into a common unit (Btu), and reporting the combined energy consumption and cost for a given facility. In the second case study for a small enterprise there is only one source of energy (electricity), and a simple spreadsheet is used to track energy consumption and cost.
Once you have determined the analysis method, assign data analysis responsibilities. Of course, the person determining the appropriate analysis method may be the person who will be performing the analysis. Some considerations for assigning the responsibilities include:
Analysis complexity
Resource availability
Data access
Experience with process/equipment being analyzed
Need for results
Analysis complexity – Competency was discussed previously, and the analysis capabilities within your organization can dictate the method of analysis. On the other hand, if a desired method of analysis is necessary to obtain the desired results for evaluating energy performance, person(s) responsible for the analysis must have the necessary competency to handle the analysis complexity. An example:
Electricity consumption for lighting in a windowless office will be directly related to occupancy and can be demonstrated with a simple graph and easily understood by personnel with basic training. Electricity consumption for air conditioning can be affected by weather, time of the year, time of day and occupancy and could require complicated statistical techniques to analyze the relationship between consumption and all these factors and may require someone with a high level of competency in statistical techniques.
Resource availability – In addition to being competent, the person(s) conducting the analysis may need resources such as time, data and appropriate analysis equipment such as calculators or computers.
Data access – Data access may include physical access to the equipment for data collection or access to the personnel who are responsible for the data collection. Some data may be in locations that are difficult to access and may require assistance or protective or other special equipment for collection. Data must be available on a regular basis in order to conduct a regular, consistent analysis.
Experience with process/equipment being analyzed – Personnel who are experienced with the process or equipment being analyzed bring an extra measure of knowledge that can be useful to those responsible for data analysis. Organizations may consider forming data analysis teams that include process and equipment expertise as well as analysis expertise.
Need for results – When an accurate analysis is necessary for someone in your organization to perform his or her job, that person will have an interest in ensuring the data is accurate and the analysis is correct. Someone with a vested self-interest will be a candidate for assigning the data analysis responsibility.
2.4.2 Analyze past and present energy use and consumption
Data needs were identified in Step 2.2.3, and analysis of past and present energy use and consumption are a part of the energy review.
The DOE Energy Footprint tool referred to in Steps 2.3.3 and 2.4.1 can help you perform fundamental analyses of past and present energy use and consumption, including:
Calculating the annual energy consumption and costs for all fuel sources and all years of energy data
Plotting energy consumption of individual and combined fuels by month and year
Generating an energy balance using a “bottom up” energy calculation of your major energy equipment, systems and processes to allow a comparison to the “top down” total facility or building energy consumption. This could be done as a precursor to determining your significant energy uses. (A “bottom-up” energy analysis estimates the energy consumption by large equipment, system or process energy uses.)
Once you have determined these parameters, however, continue to regularly collect and update the data to monitor conditions in the EnMS so you can make changes as required. Organizational changes related to processes, equipment, occupancy, improvement projects, etc. may require adjustments in your EnPIs, baselines, SEUs, objectives and targets or other parts of the EnMS. Continue to collect data to evaluate any required adjustments to energy metrics or energy performance.
Energy uses were identified in Step 2.2.2. Ongoing analysis of data for these energy uses will help determine whether the delineation of uses remains relevant to the EnMS and to identify potential improvement opportunities. Things to consider during the analysis include:
Do energy uses lend themselves to data collection?
Is the data collected sufficient for energy use evaluation?
Is metering needed to collect data for evaluation of energy uses?
Can energy performance be evaluated with the current energy uses?
Do energy uses need modification because of organizational or other changes?
Do energy uses account for all your organization’s energy?
Would a different energy source result in better efficiency or energy utilization?
Energy consumption analysis will initially consist of determining the big energy consumers and addressing the components of the energy review and other metrics as discussed in Step 2.2.3. Continue collecting and analyzing consumption data to:
Evaluate results of improvement projects
Ensure operational consistency
Verify continued relevancy of significant energy uses
Evaluate effects of process changes or additions
Identify areas for improvement
Evaluate energy performance
Use the methods you developed in Step 2.4.1 to analyze the data and make changes to the system for continuous improvement. Additional discussion on monitoring and analyzing energy consumption is provided in Step 4.1.1.
Resources & Examples
The following resources provide examples of energy data tracking:
