Princeton Scorekeeping Method Research Articles

Energy signature models of naturally ventilated hotels in Athens: a hotel classification methodology

ABSTRACTNowadays, improving the efficiency and environmental performance of buildings is becoming an increasingly important issue and a requirement. This study proposes a hotel classification methodology for naturally ventilated hotels based on an energy audit in 35 hotels in Athens. By considering energy consumption and monthly temperature, the energy signature model, Princeton Scorekeeping Method, is adjusted to the hotels’ operating characteristics. The energy signature models of the sample hotels are studied in detail and three types of hotels are identified: the ‘annual’, ‘winter’ and ‘summer’ types, which are further examined in relation to the heating and cooling system they use. The proposed energy hotel classification represents an invaluable tool first at a micro-scale for hotel owners and managers and second at a macro-scale for policy-makers. Finally, the study examines the relation between the hotels’ occupancy patterns and energy consumption.

Virtual home energy auditing at scale: Predicting residential energy efficiency using publicly available data

In this study we model and examine the energy efficiency profile of individual single-family houses from Gainesville, Florida, in our sample (n=7091). For this we use Princeton Scorekeeping Method (PRISM) which processes historical weather data and monthly utility usage data as inputs using an iterative regression approach to compute three energy efficiency parameters: (1) baseload consumption for end-uses which do not change with weather, e.g., lighting, refrigerator, water heater; (2) heating/cooling slope which is a function of the building shell insulation and the efficiency of the heating/cooling unit; (3) reference temperature, i.e., the outside temperature at which the house turns on heating/cooling. These parameters make up the normalized annual consumption (NAC). We then proceed to regress these parameters against the publicly available data to study the extent we can extract statistical insight for residential energy efficiency profiling using publicly available information (n=5243). These regression models are to pave a path to creating energy efficiency “reservoir maps” across individual homes and reducing the information barrier to energy efficiency adoption.

Energy Consumption and Environmental Impact Assessment of the Energy Efficient Houses in Toronto, Canada

The two identical energy efficient houses, “Energy Star” House (EH) and “Green” House (GH), located in Ontario were chosen to investigate the effectiveness of energy efficiency measures in dwellings. EH was built based on the Energy Star standard while GH had a number of additional energy savings and sustainable green features. Both houses’ energy consumption were simulated and their utility bills weather normalized by using HOT2000 building energy simulation software and Princeton Scorekeeping Method (PRISM). Although GH was expected to consume less energy than EH, considering GH's more energy efficient mechanical system and building envelope, this study showed that EH consumed less energy than GH (annual energy consumption of EH is 44% less than GH). Further investigation on occupants’ behavior and usage of the both houses using a survey showed clearly that having energy efficient systems do not necessarily lead to lower energy consumption.

A discussion on potentials of saving energy use for commercial buildings in Hong Kong

A typical commercial building was selected for a detailed study of energy use by mechanical ventilation and air-conditioning (MVAC) systems. Data from preliminary building energy auditing were analyzed and compared with the energy use of other similar buildings. Correlation relationships between the building envelope heat gain and the electrical energy use for MVAC system with key parameters were derived with approaches similar to the Princeton Scorekeeping Method (PRISM). Further, the energy simulation program TRACE 600 was used to justify the results. A total of 20 energy-saving measures were investigated for possible use in local commercial buildings. The results provided are useful for engineers in designing energy-efficient commercial buildings or generating new design concepts for better MVAC systems.

A Change-Point Principal Component Analysis (CP/PCA) Method for Predicting Energy Usage in Commercial Buildings: The PCA Model

A new method for predicting daily whole-building electricity usage in a commercial building has been developed. This method utilizes a Principal Component Analysis (PCA) of intercorrelated influencing parameters (e.g., dry-bulb temperature, solar radiation and humidity) to predict electricity consumption in conjunction with a change-point model. This paper describes the PCA procedure and presents the results of its application in conjunction with a change-point regression, to predict whole-building electricity consumption for a commercial grocery store. Comparison of the results with a traditional Multiple Linear Regression (MLR) analysis indicates that a change-point, Principal Component Analysis (CP/PCA) appears to produce a more reliable and physically plausible model than an MLR analysis and offers more insight into the environmental and operational driving forces that influence energy consumption in a commercial building. It is thought that the method will be useful for determining conservation retrofit savings from pre-retrofit and post-retrofit consumption data for commercial buildings. A companion paper presents the development of the four-parameter change-point model and a comparison to the Princeton Scorekeeping Method (PRISM) (Ruch and Claridge, 1991).

Measuring the components of regional energy conservation: Residential natural-gas consumption in St. Louis

We present results of a study of changes in residential natural-gas consumption in the St. Louis region. The Princeton Scorekeeping Method (PRISM) is used for measuring changes in weather-adjusted consumption and for isolating the components of conservation. PRISM also provides a basis for separating changes in the thermal integrity of the housing stock from changes in average indoor temperatures. We decompose savings further by considering demographic variables, fuel substitution, and the composition of internal heat gains. The results show that a substantial fraction of savings can be attributed to changes in weather-independent consumption (30–40% of total savings for St. Louis since 1971), but a considerable fraction of this may be caused by changes in household size. Savings associated with structural improvements to the housing stock, which generally have long-term effects on energy consumption, represent the principal component of savings for St. Louis (about 53%). Savings from behavioral changes that involve lower indoor temperatures and possibly decreased comfort may be readily implemented by households as a first reaction to fuel shortages or sudden price increases, but at least in some areas of the country, they have had little, if any, lasting effect. Over the long term, the principal cause of conservation may be increases in energy prices. For St. Louis, 85% of the change in natural-gas consumption since 1971 can be explained by variations in price alone. The remaining unexplained variation may be correlated with other factors such as changes in energy awareness. The sources of conservation which are identified in analyses of this type are important for developing effective conservation policies.

Comparison of a weather-adjustment method with end-use load data for residential electricity use

Because weather has a substantial effect on energy use, it is important to adjust (normalize) for variations in weather, both across space and time. The Princeton Scorekeeping Method (PRISM) is probably the most widely used method to adjust residential energy use data for differences in winter severity. This paper compares PRISM results with submetered data obtained from homes in Hood River, Oregon. The data were collected as part of the Hood River Conservation Project (HRCP), a major residential retrofit demonstration project. HRCP data include whole-house and space-heating electricity uses, recorded at 15-minute intervals, NOAA weather data, and results of a detailed on-site home interview with these households. We compared PRISM estimates of total and space-heating electricity uses with annual aggregates of the load data. These comparisons were conducted for the subset of 71 homes that used more than 4000 kWh year for space heating. PRISM estimates of total electricity use are in almost perfect agreement with the annual totals of the whole-house load channel. PRISM estimates of space-heating electricity use are generally higher than the annual totals of the space-heating load channel, by an average of 600 kWh year (6%).

PRISM applied to a multifamily building: The lumley homes case study

The gas consumption for space and water heating and for cooking in a multifamily building is analyzed using the Princeton Scorekeeping Method (PRISM). An objective of the study is to explain in physical terms the components of annual gas consumption, and to discuss how physical parameters and their changes can be observed in the data. The three-parameter model of PRISM is estimated using both monthly billing data and daily measurements obtained from instrumentation installed in the building. The effects on parameter estimates of known physical characteristics, namely high indoor temperatures and seasonal changes in base-level consumption, are discussed. The effects of unknown physical characteristics, namely heating system efficiency and infiltration rate, are inferred from the analysis. These variables are estimated using an extended PRISM model. We conclude that careful consideration of the building's operation and other supplemental data permit physical interpretations of parameter estimates that are not possible with a straightforward a application of PRISM alone. Departures from the validity of the assumptions of constant base level, constant heating slope, and constant reference temperature throughout a season are examined.

Energy conservation in low-income homes in New York City: the effectiveness of house doctoring

The Princeton Scorekeeping Method (PRISM) has been used to analyze the results of energy conservation measures applied to a sample of low-income homes in New York City. Of particular interest is a comparison of conventional auditing with the use of the “house doctor” treatment. PRISM indicates that the house doctor approach, including follow-up retrofits, led to average fuel savings of 20.2%, while the conventional approach produced savings of only 2.5%. The house doctor approach achieved significantly greater energy savings for both rowhouses and detached homes. Significant differences are found between savings achieved in rowhouses in a low-income area of the City, which showed overall savings of 22.0%, and detached homes in middle-income sections of the City, where the savings averaged 8.3%. A restricted form of the PRISM method, with the reference temperature fixed at 15.6°C (60°F) but including the fuel consumed during the retrofit period, has been used to analyze an expanded set of homes. Similar results are found.

A midwest low-income weatherization program seen through PRISM

The application of the Princeton Scorekeeping Method (PRISM) to an evaluation of a low-income weatherization program in Wisconsin is described. Detailed profiles are presented of the weatherized houses and the work done. Savings are found to be on the order of 6% to 10%. Higher savings appear to be associated with higher initial consumption levels, with older and larger houses, and with greater weatherization investments, suggesting that a focus on houses with high savings potential may be cost-effective in planning future weatherizations.

The modular retrofit experiment: design, scorekeeping and evaluation

The Modular Retrofit Experiment (MRE) was a collaboration between five gas utilities and Princeton designed to provide well documented measurements of energy savings associated with house retrofits. Two retrofit strategies were evaluated — a one-day visit by “house doctors”, using instrumented energy analysis, and the same house-doctor treatment followed by additional, major retrofits. The 138 houses in this experiment included a group of control houses that received no treatment. All three groups showed significant energy savings, measured using the Princeton Scorekeeping Method (PRISM). Median percent savings (± standard errors) for the control, house-doctor, and major-retrofit houses were 9.8 (±1.4), 15.3 (±1.9), and 21.9 (±1.2), respectively. The savings in the control group closely agree with the average gas savings in the utility service areas over the same period of time. Our analysis indicates that the full potential of the one-day house-doctor visit was not realized in the MRE. Nevertheless, estimates of costs and savings reveal that house doctoring was more cost-effective than the more conventional measures installed in the subsequent contractor retrofits.