1、Proceedings 1999 National Commissioning Conference. Page 1Presented at National Commissioning Conference, 1999.Use of Billing Simulation Tool for CommissioningDavid Robison, Howard ReichmuthStellar Processes, Inc.SynopsisA spreadsheet tool has been developed that allows quick adjustment of a simplif
2、ied engineering model to match actual utility bills. The tool utilizes billing analysis of commercial facilities to: Diagnose energy patterns and end use consumption; Calibrate savings estimates to agree with actual usage; Verify vendor claims for energy products and services; Generate performance t
3、argets and compare against actual energy bills. This application represents a low-cost, simplified commissioning check.The tool is designed to operate with only simple information about the facility and to focus on the HVAC system. It represents one quick approach to treating the facility as an inte
4、grated whole. Case examples illustrate how the tool is useful in diagnosing energy problems, guiding on-site audits, establishing predicted targets for O Explicit inputs allowing changes for operations or equipment efficiency; Results based on the actual local weather, not average weather; Graphic o
5、utputs that are readily understood by the customer. Proceedings 1999 National Commissioning Conference. Page 5Benchmark Comparison ofRealization Rates0.00.51.01.52.02.53.00.0 1.0 2.0 3.0DOE-2 ModelSimulation ToolFigure C. Benchmark ComparisonVerification CasesThe following examples illustrate some o
6、f the ways the tool can be useful.Large OfficePredicted and Actual Energy ConsumptionUsing Actual Weather and Occupancy0200,000400,000600,000800,0001,000,0001,200,000Jan-92Feb-92Mar-92Apr-92May-92Jun-92Jul-92Aug-92Sep-92Oct-92Nov-92Dec-92ElectricityUsage, kWh/Month PredictedConsumptionActualConsumpt
7、ionBaselineConsumptionFigure D. Large OfficeThis retrofit project was extensively commissioned including functional performance tests of equipment as installed, review of trend logs and short-term monitoring. The monitoring revealed that some initial modeling assumptions were incorrect. Specifically
8、, plug loads and night fan Proceedings 1999 National Commissioning Conference. Page 6usage were higher than assumed. It was, however, not feasible to redo the expensive DOE2 model for such small changes. Despite the detailed information, the service company was not able to provide the customer with
9、a concise statement of exactly what monthly savings were accomplished.The simplified model in Figure D was corrected for the changes revealed by monitoring but otherwise matches the DOE-2 model. Results show that actual savings are about 33% rather than the predicted 41%, with the difference explain
10、ed by the monitored changes. The simplified model is better able to show the comparison because it provides results based on the actual weather compared to the actual post-retrofit bills.SupermarketPredicted and Actual Energy ConsumptionUsing Actual Weather and Occupancy020,00040,00060,00080,000100,
11、000120,000140,000160,000180,000Jan-98Feb-98Mar-98Apr-98May-98Jun-98Jul-98Aug-97Sep-97Oct-97Nov-97Dec-97ElectricityUsage, kWh/Month PredictedConsumptionActualConsumptionBaselineConsumptionFigure E. Initial Supermarket BillingsThis example shows the commissioning graph for a supermarket that conducted
12、 lighting retrofit. At the same time, they also added a number of additional energy-efficient refrigeration cases. The customer notes that his bills have not changed and wonders if the efficiency measures have been effective. The results in Figure E are ambiguous. Any decrease in the monthly bill is
13、 small due to the added equipment and the variability of operations.Using the model, we are able to estimate what the old store would have used with the old lighting and the old type of refrigeration for the new cooler cases. This “hypothetical“ baseline provides a better representation of what the
14、customers bills would have been for purposes of estimating savings. In Figure F, the difference between the hypothetical basecase and the actual bills is more apparent. Based on this graph, the efficiency measures appear to be effective.Proceedings 1999 National Commissioning Conference. Page 7Predi
15、cted and Actual Energy ConsumptionUsing Actual Weather and Occupancy020,00040,00060,00080,000100,000120,000140,000160,000180,000200,000Jan-98Feb-98Mar-98Apr-98May-98Jun-98Jul-98Aug-97Sep-97Oct-97Nov-97Dec-97ElectricityUsage, kWh/Month PredictedConsumptionActualConsumptionBaselineConsumptionFigure F.
16、 Revised SupermarketIt must be noted that the customer may be skeptical of introducing a hypothetical baseline. The key to this approach lies in first demonstrating with the operational plot or tuning graph, that the modeler has accurately and fairly represented the buildings performance. It is impo
17、rtant that the methodology be transparent to the customer so that the extrapolating to a revised baseline will appear fair to both parties.Retail StoreTwo Year Billings0123420 40 60 80Mean Monthy Temp, Deg FNormalized Power, watts/ft2First Year ElectricBillsSecond YearElectric BillsBasecase Electric
18、ModelComparison CaseElectric ModelFigure G. Retail Store Tuning GraphThe retail store in this example conducted a successful lighting retrofit and dramatically reduced electric bills. They took advantage of the savings to restore air conditioning, which they had earlier chose to minimize. As Figure
19、G shows, operations in the store have changed so that one can no longer directly compare pre- and post-retrofit bills in order to compute savings. However, the model can be used to estimate what bills would have been had air conditioning been in use during the pre-retrofit period. Proceedings 1999 N
20、ational Commissioning Conference. Page 8Small OfficePredicted and Actual Energy ConsumptionUsing Actual Weather and Occupancy050100150200250300Jan-95Feb-95Mar-95Apr-95May-95Jun-95Jul-95 Aug-95Sep-95Oct-95Nov-95Dec-95Gas Usage, Therm/Month PredictedConsumptionActualConsumptionBaselineConsumptionFigur
21、e H. Small Office Pre and Post WeatherizationFigure H shows a “commissioning“ graph of fuel use in government facility. Weatherization occurred during the summer. Thus, the energy usage does not match predictions during spring but matches well during fall. The facility manager was pleased with this
22、graph because he had never before received confirmation that weatherization efforts were successful. Continued production of this graph on a routine basis provides a check to assure that O&M measure savings persist over time. This example demonstrates how the billing analysis may be used verify inst
23、allations for projects that would otherwise be too expensive to permit detailed commissioning.SchoolFigure I shows post-retrofit bills for a junior high school that conducted a lighting retrofit. As is apparent in the plot, the post-retrofit bills have not decreased as much as expected. Upon investi
24、gation, it turned out that the school operations changed with the addition of a community basketball camp during evening hours. For an accurate picture of the savings, operating hours need to be extended.Proceedings 1999 National Commissioning Conference. Page 9Predicted and Actual Billings, Post Re
25、trofit012345620 30 40 50 60 70 80Mean Month Temp, deg FNormalized Power, watts/ft2 Baseline ElectricityActual Electric BillsEfficient ElectricFigure I. School Operations PlotRecommissioningThe tool is useful in the context of “prospecting for savings“ or identifying potential existing buildings that
26、 could benefit from recommissioning. Older buildings may exhibit high energy usage that cannot be explained by lighting or space conditioning alone. This is a clue that the HVAC system is inefficient and a good candidate for commissioning.Predicted and Actual Billings024681020 30 40 50 60 70 80Mean
27、Month Temp, deg FNormalized Power, watts/ft2PredictedElectricityPredictedFuelActualElectricBillsActual FuelBillsFigure J. HospitalFigure J shows billing data from a small-town hospital. It is apparent that a gas boiler runs during all seasons, perhaps to supply hot water - an obvious opportunity for
28、 more efficient fuel use. Similarly, space heating is high, indicating the need for boiler tune-up or weatherization measures. However, electric consumption is low, suggesting partial occupancy or low utilization. This facility would not be a good candidate for acquiring electric savings. In this ca
29、se, review of the billing data before conducting a site visit may save program expenses by helping to eliminate poor candidates.Proceedings 1999 National Commissioning Conference. Page 10Predicted and Actual Billings0123456720 30 40 50 60 70 80Mean Month Temp, deg FNormalized Power, watts/ft2Predict
30、edElectricityActualElectric BillsFigure K. Existing OfficeFigure K shows the billing data from an existing office building that has never been commissioned. Usage is much higher than can be explained by lighting alone. The tuning process suggests an inefficient HVAC system with leaky dampers and exc
31、essive terminal reheat. This facility would be a good candidate for recommissioning.ConclusionsA simplified modeling tool has been developed to link utility bills and engineering simulation modeling. The tool has been demonstrated to provide similar results to DOE-2, but with greatly reduced data re
32、quirements. The primary advantage is the ability to quickly match the model to actual bills, providing a tuned, as-built model. The process of tuning often reveals opportunities for operational improvements. The completed model facilitates extrapolation from current operations to estimates of annual
33、 energy consumption. Modeling can also provide performance targets to be compared against post-retrofit utility bills. This check represents a simple-level form of commissioning that can be conducted at low cost. It also provides a mechanism for on-going quality assurance to verify that operational improvements persist over time.
