Proceedings 1999 National Commissioning Conference.Page 1Presented at National Commissioning Conference, 1999.Use of Billing Simulation Tool for CommissioningDavid Robison, Howard Reichmuth Stellar Processes, Inc.SynopsisA spreadsheet tool has been developed that allows quick adjustment of a simplified 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 targets 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 integrated 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 outputs that are readily understood by the customer. Proceedings 1999 National Commissioning Conference.Page 5Benchmark Comparison ofRealization Rates0.00.51.01.52.02.53.00.01.02.03.0DOE-2 ModelSimulation ToolFigure C. Benchmark ComparisonVerification CasesThe following examples illustrate some of the ways the tool can be useful.Large OfficePredicted and Actual Energy Consumption Using Actual Weather and Occupancy0200,000400,000600,000800,0001,000,0001,200,000Jan- 92Feb- 92Mar- 92Apr- 92May- 92Jun- 92Jul- 92Aug- 92Sep- 92Oct- 92Nov- 92Dec- 92Electricity Usage, kWh/MonthPredicted ConsumptionActual ConsumptionBaseline ConsumptionFigure 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, 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 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 explained 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 Consumption Using Actual Weather and Occupancy020,00040,00060,00080,000100,000120,000140,000160,000180,000Jan- 98Feb- 98Mar- 98Apr- 98May- 98Jun- 98Jul- 98Aug- 97Sep- 97Oct- 97Nov- 97Dec- 97Electricity Usage, kWh/MonthPredicted ConsumptionActual ConsumptionBaseline ConsumptionFigure E. Initial Supermarket BillingsThis example shows the commissioning graph for a supermarket that conducted 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 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 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 7Predicted and Actual Energy Consumption Using 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- 97Electricity Usage, kWh/MonthPredicted ConsumptionActual ConsumptionBaseline ConsumptionFigure F. 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 important that the methodology be transparent to the customer so that the extrapolating to a revised baseline will appear fair