Engineering topics

Price, Phillip N

Publications and source records attributed to Price, Phillip N.

5 records

DOE OSTI2011

Using Whole-Building Electric Load Data in Continuous or Retro-Commissioning

Whole-building electric load data can often reveal problems with building equipment or operations. In this paper, we present methods for analyzing 15-minute-interval electric load data. These methods allow building operators, energy managers, and commissioning agents to better understand a building's electricity consumption over time and to compare it to other buildings, helping them to 'ask the right questions' to discover opportunities for electricity waste elimination, energy efficiency, peak load management, and demand response. For example: Does the building use too much energy at night, or on hot days, or in the early evening? Knowing the answer to questions like these can help with retro-commissioning or continuous commissioning. The methods discussed here can also be used to assess how building energy performance varies with time. Comparing electric load before and after fixing equipment or changing operations can help verify that the fixes have the intended effect on energy consumption. Analysis methods discussed in this paper include: ways to graphically represent electric load data; the definition of various parameters that characterize facility electricity loads; and a regression-based electricity load model that accounts for both time of week and outdoor air temperature. The methods are illustrated by applying them to data from commercial buildings. We demonstrate the ability to recognize changes in building operation, and to quantify changes in energy performance. Some key findings are: 1) Plotting time series electric load data is useful for understanding electricity consumption patterns and changes to those patterns, but results may be misleading if data from different time intervals are not weather-normalized. 2) Parameter plots can highlight key features of electric load data and may be easier to interpret than plots of time series data themselves. 3) A time-of-week indicator variable (as compared to time-of-day and day-of-week indicator variables) improves the accuracy of regression models of electric load. 4) A piecewise linear and continuous outdoor air temperature dependence can be derived without the use of a change-point model (which would add complexity to the modeling algorithm) or assumptions about when structural changes occur (which could introduce inaccuracy). 5) A model that includes time-of-week and temperature dependence can be used for weather normalization and can determine whether the building is unusually temperature-sensitive, which can indicate problems with HVAC operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2010

Quantifying Changes in Building Electricity Use, with Application to Demand Response

We present methods for analyzing commercial and industrial facility 15-minute-interval electric load data. These methods allow building managers to better understand their facility's electricity consumption over time and to compare it to other buildings, helping them to ask the right questions to discover opportunities for demand response, energy efficiency, electricity waste elimination, and peak load management. We primarily focus on demand response. Methods discussed include graphical representations of electric load data, a regression-based electricity load model that uses a time-of-week indicator variable and a piecewise linear and continuous outdoor air temperature dependence, and the definition of various parameters that characterize facility electricity loads and demand response behavior. In the future, these methods could be translated into easy-to-use tools for building managers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2010

Occupancy-Based Energy Management in Buildings: Final Report to Sponsors

The Lawrence Berkeley National Laboratory (LBNL), the University of California Merced (UCM), and the United Technologies Research Center (UTRC) conducted field studies and modeling analyses in the Classroom and Office Building (COB) and the Science and Engineering Building (S&E) at the University of California, Merced. In the first year, of a planned multiyear project, our goal was to study the feasibility and efficacy of occupancy-based energy management. The first-year research goals were twofold. The first was to explore the likely energy savings if we know the number and location of building occupants in a typical commercial building. The second was to model and estimate people movement in a building. Our findings suggest that a 10-14percent reduction in HVAC energy consumption is possible over typical HVAC operating conditions when we know occupancy throughout the building. With the conclusion of the first-year tasks, we plan to review these results further before this group pursues follow-on funding.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2010

Characterizing the Response of Commercial and Industrial Facilities to Dynamic Pricing Signals from the Utility

We describe a method to generate statistical models of electricity demand from Commercial and Industrial (C&I) facilities including their response to dynamic pricing signals. Models are built with historical electricity demand data. A facility model is the sum of a baseline demand model and a residual demand model; the latter quantifies deviations from the baseline model due to dynamic pricing signals from the utility. Three regression-based baseline computation methods were developed and analyzed. All methods performed similarly. To understand the diversity of facility responses to dynamic pricing signals, we have characterized the response of 44 C&I facilities participating in a Demand Response (DR) program using dynamic pricing in California (Pacific Gas and Electric's Critical Peak Pricing Program). In most cases, facilities shed load during DR events but there is significant heterogeneity in facility responses. Modeling facility response to dynamic price signals is beneficial to the Independent System Operator for scheduling supply to meet demand, to the utility for improving dynamic pricing programs, and to the customer for minimizing energy costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2010

Intermolecular potential parameters and combining rules determined from viscosity data

The Law of Corresponding States has been demonstrated for a number of pure substances and binary mixtures, and provides evidence that the transport properties viscosity and diffusion can be determined from a molecular shape function, often taken to be a Lennard-Jones 12-6 potential, that requires two scaling parameters: a well depth {var_epsilon}{sub ij} and a collision diameter {sigma}{sub ij}, both of which depend on the interacting species i and j. We obtain estimates for {var_epsilon}{sub ij} and {sigma}{sub ij} of interacting species by finding the values that provide the best fit to viscosity data for binary mixtures, and compare these to calculated parameters using several 'combining rules' that have been suggested for determining parameter values for binary collisions from parameter values that describe collisions of like molecules. Different combining rules give different values for {sigma}{sub ij} and {var_epsilon}{sub ij} and for some mixtures the differences between these values and the best-fit parameter values are rather large. There is a curve in ({var_epsilon}{sub ij}, {sigma}{sub ij}) space such that parameter values on the curve generate a calculated viscosity in good agreement with measurements for a pure gas or a binary mixture. The various combining rules produce couples of parameters {var_epsilon}{sub ij}, {sigma}{sub ij} that lie close to the curve and therefore generate predicted mixture viscosities in satisfactory agreement with experiment. Although the combining rules were found to underpredict the viscosity in most of the cases, Kong's rule was found to work better than the others, but none of the combining rules consistently yields parameter values near the best-fit values, suggesting that improved rules could be developed.

54 ENVIRONMENTAL SCIENCES↗