Chilling at Penn: Weather-Analysis of Load Tool (WALT) Abstract: Penn’s MOD 7 plant supplies chilled water to the entire campus for its air- conditioning and other cooling needs. Currently at MOD 7, an operator selects the thermodynamic parameters of the supplied water based solely on qualitative experience. This results in volatility of the selected parameters and overconsumption of energy. We have created a model to systematically determine the parameters using the expected dry-bulb temperature, relative humidity, and cloud cover over the next 24 hours. MOD 7 operators can use the outputs of this model as the inputs into their current control center. This model can be used not only in determining the necessary plant outputs, but also as a tracking and monitoring tool. It forecasts energy consumption for the following day, allowing the plant operator to proactively plan for the costly effects associated with spikes in chilled water demand. The university can also use this tool to understand expected performance when doing life-cycle cost analysis of the plant and making upgrades.. University of Pennsylvania Department of Electrical and Systems Engineering TEAM 08 Authors: Christine Muller (SSE) Amrita Nag (SSE) Megan Purzycki (SSE) Daniel Sherman (EE) Advisors: Dr. Peter Scott Mr. Donald Bravo DEMO TIMES: Thursday, April 19 th, :30am - 12:00pm 1:30pm - 2:00pm 2:30pm - 3:00pm 3:00pm - 3:30pm Project Statement Even though the MOD 7 plant is regarded as a highly innovative chilling structure and uses many energy conservation measures, the plant lacks any systematic way of determining the parameters of the water it supplies. This causes certain parameters to be unstable, making the plant more likely to overconsume energy and causing more wear and tear on the machinery. In order to realize the savings that result from systematic determination of these parameters, there must be an accurate forecasting algorithm that predicts the chilled water demand. Figure 1. MOD 7 Chilling Plant Building the Model The model outputs predicted water flow (Q), temperature differential (ΔT) between supply and return, and energy consumption for the plant on an hourly basis using dry-bulb temperature (T), relative humidity (H), and cloud cover (C) (Figure 2). Regression equations determine the temperature differential and flow outputs (Equations 1 & 2). When creating the regression, we decided to screen the data, taking out data points where the operator overshot or undershot the parameters, to ensure our model reflects a more accurate prediction. An overshot was considered a data point that was both 15% higher than the previous point and 10% higher than the next point. Outlier data points were replaced with an average of the previous and next data points. We were aiming to get an adjusted R 2 value greater than 0.90 for both equations. A thermodynamic equation then determines energy consumption rate using the energy conversion factor of MOD7 and the outputs from the temperature differential and flow equations (Equation 3). To make the tool user friendly, we designed a system that will automatically extract the necessary inputs from the internet and display the final outputs in both table and graphical formats in a user interface (Figure 3). Verifying the Model The prediction model was verified by comparing the predicted parameters to the actual values over a 1 month interval (Figure 4,5). We created a 95% Confidence Interval (α=0.05) for each prediction to counteract the margin of error that occurs when using regression analysis (Equation 4). Over 94% of the actual values fell within our CI. Differences can be attributed to random fluctuations in building occupancy that could not be incorporated into regression. System Block Diagram InputsOutputsOutput Dry Bulb Temp ( o F) Cloud Cover (1-10) Relative Humidity (%) Linear Regression Chiller Efficiency (Tons/kW) Heat Removed (Tons) Temp Differential ( o F) Water Flow Rate (GPM) Water Flow Rate (GPM) Energy Consumption Rate (kW) Figure 2. System Block Diagram Project Objectives The objective of this project was to design a forecasting model using screened historical data and regression analysis. Our intention was to make a tool that the university’s Facilities and Real Estate Services (FRES) could use for the following purposes: Completely automated system to reduce the volatility in the thermodynamic parameters and the training time for new personnel with insufficient prior experience. The system makes predictions 24 hrs in advance, allowing operators to plan for spikes in chilled water demand or any other drastic deviations much sooner. Diagnostic tool to monitor the actual performance of the plant compared to the expected performance, allowing operators to recognize if any machinery is malfunctioning. Benchmarking tool to compare expected performance of the plant vs. historical performance when any upgrades are made in the future. Conclusion We automated selecting MOD 7’s chilled water supply conditions. While creating our model, we discovered that the plant does not always operate as efficiently as it can. The historical data contained several outliers that resulted from the operator misestimating demand. Using statistical analysis based on the weather forecast, on average, consumes less energy than the way the operators are currently predicting demand (Figure 6). We calculated cost savings of $3817 from using our model during the 1 month verification interval. This can equate to much higher savings over a longer time period. If FRES adopts this model, this university facility will operate more efficiently, conserve energy, and realize significant cost savings. Equation 4. Confidence Interval for Flow FLOW (GPM) = T H – C – R adj 2 = TEMPERATURE DIFFERENTIAL ( o F) = 0.169T H – R adj 2 = RATE OF ENERGY CONSUMPTION (kW)= 0.60×Q×ΔT 24 Equations 1, 2, 3. Final Model Equations Figure 6. Actual Energy Consumed – Model Energy Requirement : 2/20/12 - 3/20/12 Time Energy Consumption (kW) Figure 3. Partial User Interface