GENERAL_ASPECTS_OF_ENERGY_MANAGEMENT_AND_ENERGY_AUDIT
(CHAPTER 9:ENERGY MONITORING AND TARGETING)
Introduction
It is possible to establish the existing energy consumption of a facility or organization through an energy audit. However, this only produces a ‘picture’ of past energy consumption. In order to keep control of subsequent energy consumption, it is necessary to initiate a monitoring programme. However, monitoring programme alone has limited value as it simply records energy consumption. To achieve improvements in energy performance, a targeting programme, in which targets are set, must accompany the monitoring process and planned improvements made.
Energy monitoring and targeting (M&T) is primarily a management technique that uses energy information as a basis to eliminate waste, reduce and control current level of energy use and improve the existing operating procedures. It is based on the principle “you can’t manage what you don’t measure’. It essentially combines the principles of energy use and statistics.
By using M&T, all plant and building utilities such as fuel, steam, refrigeration, compressed air, water, effluent, and electricity are managed as controllable resources in the same way that raw materials, finished product inventory, building occupancy, personnel and capital are managed.
Monitoring and Targeting (M&T) programs have been so effective that they show typical reductions in annual energy costs in various industrial sectors between 5 and 15%.
What is Monitoring & Targeting?
Monitoring is the process of establishing the existing pattern of energy consumption and explaining deviations from existing pattern. Its primary goal is to maintain existing pattern by providing all the necessary data on energy consumption and key related data such as production.
Targeting is the identification of desirable energy consumption level and working towards achieving them. Targets are based on the historical (average or best) data acquired during the monitoring as well as benchmarking with energy performance of similar organizations.
Setting up Monitoring & Targeting
Before initiating M&T, it is important to establish Energy Account Centers (EACs) within an organization. These may be departments, processes or cost centers. Operational managers should be accountable for the energy consumption of the EACs for which they are responsible.
It is important that any proposed M&T programme be designed to suit the needs of the particular organization. From an energy point of view, organization can be characterized in various ways. Typical classifications are by the number of sites covered and the level of metering adopted as follows:
1.Single site with central utility metering
2.Single site with sub-metering
3. Multi-site with central utility metering
4. Multiple-site with sub-metering
Single site with central utility metering are probably best treated as a single EAC, while the introduction of sub-metering enables such site to be broken-up into a number of separate EACs. Where the organization has a number of separate sites, each with central utility meters, the sites should be treated as separate EACs. If the organization has multiple sites, each containing sub-metering, then it should be possible to divide each site into a number of separate EACs.
Key Elements of Monitoring & Targeting System
The key elements of M&T system are:
1. Recording -Measuring and recording energy consumption of each EAC within an organization. This involves setting up procedures to ensure regular collection of reliable energy data.
2.Analysing & Comparing —Relating energy consumption to a measured output, such as production quantity for 12-24 months of historical data to obtain standard energy performance for each EAC. Standard energy performance is established through regression analysis of past data. If these data do not exist, then it will be necessary to conduct an energy audit to establish standard energy performance. Standard Energy Performance provides a base line for the assessment of future performance. It can also be used as an initial target.
3.Setting Targets -Setting energy targets for each EAC. Energy cost savings can be consistently achieved if improvements are made on standard energy performance. Achievable targets should therefore be set which improve on standard energy performance. Targets can be set based on external benchmarking with other similar organization or historical achievement of least energy consumption in the same organization.
4.Monitoring -Comparing actual energy consumption to the set target on a regular basis
5. Reporting -Reporting the results to management including any variances from the targets which have been set and related performance problems in equipment and systems. Energy management reports should be produced for each EAC on a regular basis. These reports provide the stimulus for improved energy performance, and should also quantify any improvements that are achieved.
6.Controlling -Implementing management measures to correct any variances, which may have occurred Particularly M & T system will involve the following:
¢ Checking the accuracy of energy invoices
¢ Allocating energy costs to specific departments (Energy Accounting Centres)
¢ Determining energy performance / efficiency
¢ Recording energy use, so that projects intended to improve energy efficiency can be checked for results.
¢ Highlighting performance problems in equipment or systems
Benefits of M&T
The ultimate goal is to reduce energy costs through improved energy efficiency and management control measures. Other benefits include:
1.Identify and explain an increase or decrease in energy use
2.Draw energy consumption trends (weekly, seasonal, operational)
3.Improve energy budgeting corresponding to production plans
4.Observe how the organization reacted to changes in the past
5.Determine future energy use when planning changes in operations
6.Diagnose specific areas of wasted energy
7.Develop performance targets for energy management programs / energy action plans
8.Manage energy consumption rather than accept it as a fixed cost that cannot be controlled.
Data and Information Sources
Information related to energy use may be obtained from following sources:
1.Plant level information can be derived from financial accounting systems—utilities cost centre
2.Plant department level information can be found in comparative energy consumption data for a group of similar facilities, service entrance meter readings etc.
3.System level (for example, compressor house) performance data can be determined from submetering data
4. Equipment level information can be obtained from nameplate data, run-time and schedule information, sub-metered data on specific energy consuming equipment.
All of these data are useful and can be processed to yield information about facility performance.
Data and Information Analysis
There are a wide variety of statistical and numerical techniques which can be used to understand why energy is consumed in a particular way. Some of the analysis techniques are fairly simple and can be done with hand calculator, the others are more complex and may require use of information technology.The various analysis techniques are explained as follows:
Annual Energy Consumption
The simplest analysis is to produce percentage breakdown of annual energy consumption and cost data. This is a useful technique which enables to assess overall energy performance of a plant or building quickly and easily. The analysis of annual energy consumption should be carried out as follows:
1. Convert all the energy consumption data into standard units (usually the kcal) using the standard energy conversions shown in Table 9.1.
2.Compile the data as shown in Table 9.2 showing the annual energy consumption and cost for various fuel and energy types.
3.Compile the above information to produce percentage breakdown of the total energy consumption and cost of each energy type (Table 9.3).
4.Produce pie-charts similar to those shown in Figure 9.1 and Figure 9.2 to show graphically the energy and cost contribution of each energy type
5.Similar procedure can be followed for previous years to identify trends.
It may be noted that this simple analysis identifies energy consumption and cost breakdown and trends and does not make any allowance for variable factors which may influence energy consumption e.g. climatic zones or occupancy for buildings. Hence, this analysis cannot be used as a comparison tools between different organizations.
Annual Energy Consumption Using Bar Chart
If 24 months of energy data is collated, the annual energy consumption can be represented in form of bar chart. The most common bar chart application used in energy management is one showing the energy per month for current year and previous year (see Figure 9.3) — however, this chart does not tell us clearly about any trends in energy consumption.
Time-dependent Energy Analysis
If monthly energy consumption data is collected, it is possible to produce a simple graph in which energy consumption is plotted against time (Figure 9.4). Through this simple time-dependent analysis, it is possible to identify general trends and seasonal patterns in energy consumption. This enables exception to the norms to be identified immediately.
The limitations of this tool are that it is difficult to find out why certain trends occur or if a particular trend is there or not. Although, this tool is useful, it can be used only as a comparative tool and not an absolute one.
Consider Figure 9.4, the time dependent graph shows monthly electricity consumption for the years 2008 and 2009. It can be seen from the graph that:
¢ Electricity consumption during the months of January, February and March of 2009 is consistently less than in the corresponding period of 2008.
¢ The base load electricity consumption is approximately 10,500 kWh/month.
¢ Energy consumption during the months of November and December 2009 and January 2010 appears to have increased significantly compared with the corresponding 2008 figures. This tends to indicate loss of control over electricity consumption.
However, it is impossible to identify why the energy consumption for January, February and March 2009 is lower than for the same period in 2009. In order to do so, further analysis is needed.
It is possible to plot more than one variable using this chart, for example, oil consumption along with
electricity consumption data.
Norm Chart
The norm chart is a sequential plot of actual energy consumption overlaid on a plot of target consumption. It is of little value as an analytical tool, but can be useful for highlighting exceptions and communicating these to managers. Because norm charts represent a historical record of energy consumption, senior and operational managers find them relatively easy to understand. Refer Figure 9.5.
Deviance Chart
Deviance charts plot the difference between target and actual energy consumption (see Figure 9.6). If,
in any one month, energy consumption is above the target value, then the consumption is plotted as a
positive value; by contrast a negative value is returned if actual consumption is lower than predicted.
When producing a deviance chart it is useful to show on the graph limits of normal operation, since
this helps to distinguish between normal limits and serious deviations from the norm. Deviance charts
are particularly good at highlighting problems, so that remedial action can be taken. They can also be
used to initiate detailed exception reports.
Relating Annual Energy and Production using Bar Chart
If related production data is also available for the same 24 months period, we know that the levels of production may have effect on energy consumption and hence specific energy consumption can be calculated and plotted as bar chart as shown in Figure 9.7. The chart shows some trends — an all time low in December followed by rising trend in specific energy consumption.
If production levels are added to SEC chart as in Figure 9.8, the features of the chart become clearer. For example, the very low SEC occurred when there was a record level of production. This indicates that there might be fixed energy consumption —1.e. consumption that occurs regardless of production
levels.
Moving Annual Total
Another way of representing energy and production data is in form of a Moving Annual Total. If 12 months of energy and production data are available, we can plot the chart. For this chart, each point represents the sum of previous 12 months of data. In this way, each point has full range of the seasons,
holidays. The technique also smoothes out errors in the timings of meter readings.If energy and production are plotted in the same chart and are tracking each other as shown in Figure 9.9, it suggests that there is no cause for alarm. Any deviations in energy line has to be watched so as to identify early warning of energy waste or to confirm that energy efficiency measures are making a positive impact.
Linear Regression Analysis
Linear regression analysis is a statistical technique which determines and quantifies the relationship between variables. It is a widely used energy management tool which enables standard equations to be established for energy consumption, often from data which would otherwise be meaningless. Regression analysis overcomes the limitation of time-dependent analysis by removing the ‘time’ element from the analysis and focusing instead on the variables which influence energy consumption.
It is a versatile technique which can be used to analyse a wide variety of applications. When used as an energy management tool, the variables commonly compared are:
1.Furnace Oil consumption versus the number of units of production.
2.Electricity consumption versus the number of units of production.
3.Water consumption versus the number of units of production.
4. Electricity consumed by lighting versus hours of occupancy.
Regression analysis is very much dependent on the quality of the data used. It should therefore be treated with care. If an analysis indicates the absence of a significant relationship between two variables, it does not necessarily mean that no relationship exists. The significance of results depends on the quantity and quality of the data used, and indeed, on the variables used in the analysis. Table 9.4 shows a selection of factors which can influence energy and water consumption.
a) Single Independent Variable
XY Scatter Diagram
XY Scatter Diagram provides more understanding of relationship between energy and production. A sample XY Scatter Diagram is shown in Figure 9.10.
This chart shows a low degree of scatter indicative of a good fit. If data fit is poor, it indicates poor level of control and hence a scope for energy savings. If data fit is poor and if it is known that there should be a relationship, it indicates a poor level of control and hence a potential for energy savings. In producing the production/energy relationship chart a relationship relating production and energy consumption is obtained.
For example if energy consumption in tones of oil equivalent (i.e. a dependent variable) and productionin Metric tonnes (i.e. an independent variable) in a foundry furnace are plotted against each other on a graph some sort of relationship between the two can be obtained.
This relationship is in fact linear and it is possible to derive an equation for the best-fit straight-line curve through the points plotted on the graph. The best-fit straight-line curve is determined by summing the squares of the distances from the straight line of the various data points. Once established, this linear equation can be used to predict future energy consumption. In addition, it can be used as a standard performance equation for energy monitoring and targeting purposes.
The generic equation for a straight-line graph can be represented as:
y=c+mx
Where y is the dependent variable (e.g. energy consumption), x is the independent variable (e.g. production), c is the value at which the straight-line curve intersects the ‘y’ axis, and m is the gradient
of the straight-line curve.
In other words
Energy consumed for the period = C + m x production for the same period
If the straight line y = c + mx is best fitted to a set of data sample points; it can be shown that
Where n is the number of data points.
These equations are known as the normal equations of the problem and they can be used to establish
the values of c and m, as illustrated in the following Example 9.1.
Example
Consider a foundry which during a monitoring programme produces the following sample data:
From this equation, it can be seen that the theoretical base load for furnace is 180 mtoe.
The same relationship can be obtained by plotting in a graph as shown in Figure 9.11.
Correlation Coefficients
The regression analysis method described in Section 4.5.1 enables a best-fit straight line to be determined for a sample data set. However, in some circumstances the sample data points may be very scattered with the result that the derived equation may be meaningless. It is therefore important to determine how well the best-fit line correlates to the sample data. This can be done by calculating the Pearson correlation coefficient, which gives an indication of the reliability of the line drawn. The Pearson correlation coefficient is a value between | and 0, with a value of 1 representing 100% correlation. The Pearson correlation coefficient (r) can be determined using equation (4.4).
Table 9.5 shows minimum acceptable correlation coefficients for given numbers of data samples.
It can be seen from the Table that the correlation coefficient in Example 9.1 is very good.
b) Multi-variable analysis
Often energy consumption can be influenced by several different variables. When this is the case
the relationship can be described by the equation:
It is difficult to solve multivariable analysis by hand calculation. It is therefore advisable to use specialist computer software which can be employed to determine the statistical relationship between the variables.
CUSUM Charts
CUSUM 1s an acronym for Cumulative Sum of differences. CUSUM charts can be particularly useful why diagnosing why energy is occurring. This is principally because they identify the date of any change in energy performance. It can be particularly helpful to know when a problem first occurred as this helps to pin-point the problem and further analysis can then be made to determine its root cause. First step is to establish standard energy performance equation through analysis of data (energy and related variable e.g. production) collected during monitoring period before any interventions are made.
It can be used to establish target or baseline against which actual energy consumption can be compared.
Second step is to calculate the differences between the standard energy consumption and actual energy
consumption. Third step is determining CUSUM which is cumulative summation of differences between
actual energy consumption and targets or baseline.
CUSUM values, when plotted against time, not only helps us to know the trends, but also helps to calculate energy savings or losses when performance changes. A typical CUSUM graph follows a trend and shows random fluctuation of energy consumption and oscillation around zero (baseline or standard). This trend will continue until something happens to alter the pattern of consumption such as the effect of an energy saving measure or, conversely, a worsening in energy efficiency (poor control, housekeeping or maintenance).
CUSUM chart (see Figure 9.12) for a generic company is shown. The CUSUM chart shows what is really happening to the energy performance. From the chart, it can be seen that starting from year 2000, performance is better than standard. Performance then declined (line going up) until April, and then it started to improve until July. However, from July onwards, there is a marked, ongoing decline in performance — line going up.
When looking at CUSUM chart, the changes in direction of the line indicate events that have relevance to the energy consumption pattern. Clearly, site knowledge is needed to interpret better events related to energy consumption. For example, if there are planned changes in energy systems, adverse change in performance can be attributed to lack of control, poor housekeeping or improper maintenance.
Example : CUSUM Technique
Energy consumption and production data were collected for furnace in a foundry over a period of 18 months. During month 9, a heat recovery system was installed. Using the plant monthly data, estimate the savings made with the heat recovery system. The plant data is given in Table 9.6:
*toe = tonnes of oil equivalent.
Steps for CUSUM analysis
1.Plot the Energy — Production graph for the first 9 months
2.Draw the best fit straight line
3.Derive the equation of the line (Equation derived is E = 0.4 P + 180)
4.Calculate the standard energy consumption based on various production based on the equation
5.Calculate the difference between actual and standard energy consumption
6.Compute CUSUM
These steps are shown in the Table 9.7.
7. Plot the CUSUM graph
8. Estimate the savings accumulated from use of the heat recovery system.
From the Figure 8.10, it can be seen that the CUSUM graph oscillates around the zero line for several months and then drops sharply after month 11. This suggests that the heat recovery system took almost two months to commission and reach proper operating conditions, after which steady savings have been achieved. Based on the graph 9.10 (see Table 8.4), savings of 44 toe (50-6) have been accumulated in the last 7 months. This represents savings of almost 2% of energy consumption. CUSUM chart for last 18 months is shown in Figure 9.13.
Energy Management Information System (EMIS)
The use of specially designed information system software is advisable when operating an M&T programme. Computers should not be seen as a replacement for the energy manager, but simply as tools which enable large amounts of data to be stored and analysed in a short period of time. A number of energy management software packages are commercially available, with varying degrees of complexity. They all tend to share the following generic features:
1.A database facility, which is capable of storing and organizing large quantities of data collected
over a long period of time.
2.The ability to record energy data for all utility types, including data taken from both meters and invoices.
3.The ability to handle complex utility tariffs. Tariffs vary from place to place, and are becoming increasingly complex as competition is introduced into the utilities sector.
4.The ability to handle other related variables such as degree days and production data.
5.A data analysis facility. This is achieved by incorporating statistical analysis software into the energy management software.
6.Areporting facility, which is capable of quickly producing energy management reports.
7.With the more sophisticated energy management packages it is possible to interface the software with Building Management Systems (BMS), so that energy data can be automatically recorded on a regular basis (e.g. hourly).
One of the great advantages of computer-based systems is their database facility, which enables historical data and data from many sources to be instantly compared. This facility is particularly useful when comparing site energy costs on a utility basis and enables energy managers quickly to assess the relative performance of various EACs. In this way EACs which are under-performing can be quickly identified and remedial action taken.
Designing Information Reporting Systems
One of the major outputs of any M&T programme is the production of energy management reports. These reports perform the vital role of communicating key information to senior and operational managers, and are therefore the means by which action is initiated within an organization. In order to ensure that prompt action is taken to minimize wasteful practices, reports should be as simple as possible and should highlight those areas in which energy wastage is occurring. Reports should be published regularly so that energy wasteful practices are identified quickly and not allowed to persist for too long. Reports should be succinct, and conform to a standard format which should be generated automatically by a computer. This minimizes preparation time, and also familiarizes managers with the information being communicated.
Most M&T programmes require reports to be published weekly or monthly. Monthly reports are usually applicable to large organizations with many sites, with weekly reports being more suitable to complex high energy consuming facilities. In applications where energy consumption is particularly high, reports may be produced daily. If the reporting period is too long, energy may well be needlessly wasted before managers are notified of the problem and remedial action is taken. Yet, if the reporting period is too short this will lead to an over-complex M&T system in which too much irrelevant information requires consideration.
The primary purpose of energy management reports is to communicate effectively with senior and operational managers. They should therefore be tailored to suit the needs of their readers, with different managers within organizations requiring different levels of report. Operational managers may need weekly reports, whereas senior management may only require a quarterly review. Figure 9.14 illustrates the relationship between reporting frequency and managerial status.
One big disadvantage of producing a large number of regular reports is that they can swamp operational managers with what may appear to be irrelevant information. One good way to get around this problem is to adopt a reporting by exception system, in which reports are only generated when energy performance falls outside certain predetermined limits. This system has the great advantage that
managers only receive reports when energy performance is either poor or very good. In addition, everyone involved in the reporting process benefits from a reduced workload.
Solved Example:
The Energy- production data (for Jan-June, 2011) of an industry follows a relationship:
Calculated energy consumption = 0.5 P +220.
A Waste heat recovery system was installed at end of June 2011 and further data was gathered up to
December 2011.
Using CUSUM technique, calculate energy savings in terms of ton of oil equivalent (toe) and the reduction in specific energy consumption achieved with the installation of waste heat recovery system.
The plant data is given in the table below.
Ans:
The table below gives values of actual energy consumption vs. calculated (predicted) energy consumption from July —Dec. 2011.
Specific energy consumption monitored vs. predicted for each month. The variations are calculated and the Cumulative sum of differences is calculated from Jan-June-2011.
Energy savings achieved = 96 toe
Reduction in specific energy consumption = 96/4550 = 0.021 toe/tonne of production (Production for
6 months = 760+820+940+750+610+670 = 4550 tonnes).
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