Calibration of an energy simulation with actual data has generally been considered too difficult to be part of the energy audit procedure. The purpose of this paper is to develop a systematic method using a “base load analysis approach” to calibrate a building energy performance model with a combination of monthly utility billing data and sub-metered data such as is commonly available in large buildings in Korea. The calibration procedure was specifically developed to be suitable for use in both the audit and savings determination procedure within a retrofit process. The procedure has been visualized using a logical flow chart and demonstrated using the simulation of a 26-story commercial building located in Seoul as a case study. The results indicate that the approach developed provided a reliable and accurate simulation of the monthly and annual building energy requirements of the case study building.

This paper describes a procedure for estimating weather-adjusted retrofit savings in commercial buildings using ambient-temperature regression models. The selection of ambient temperature as the sole independent regression variable is discussed. An approximate method for determining the uncertainty of savings and a method for identifying the data time scale which minimizes the uncertainty of savings are developed. The appropriate uses of both linear and change-point models for estimating savings based on expected heating and cooling relationships for common HVAC systems are described. A case study example illustrates the procedure.

An analytical model is developed to predict the annual variation of soil surface temperature from readily available weather data and soil thermal properties. The time variation is approximated by a first harmonic function characterized by an average, an amplitude, and a phase lag. A parametric analysis is presented to determine the effect of various factors such as evaporation, soil absorptivity, and soil convective properties on soil surface temperature. A comparison of the model predictions with experimental data is presented. The comparative analysis indicates that the simplified model predicts soil surface temperatures within ten percent of measured data for five locations.

Infiltration is customarily assumed to increase the heating and cooling load of a building by an amount equal to the mass flow rate of the infiltration times the enthalpy difference between the inside and outside air—with the latent portion of the enthalpy difference sometimes neglected. Calorimetric measurements conducted on a small test cell with measured amounts of infiltration introduced under a variety of conditions show convincingly that infiltration can lead to a much smaller change in the energy load than is customarily calculated; changes as small as 20 percent of the calculated value have been measured in the cell. The data also suggest that the phenomenon occurs in full-sized houses as well. Infiltration Heat Exchange Effectiveness (IHEE), ε, is introduced as a measure of the effectiveness of a building in “recovering” heat otherwise lost (or gained) due to infiltration. Measurements show that ε increases as: (a) flow rate decreases; (b) flow path length increases; (c) hole/crack size decreases. There is a clear correlation between large values of ε and large values of the exponent, n, so fan pressurization results may be useful in predicting ε for buildings.

Following several successful applications of feedforward neural networks (NNs) to the building energy prediction problem (Wang and Kreider, 1992; JCEM, 1992, 1993; Curtiss et al., 1993, 1994; Anstett and Kreider, 1993; Kreider and Haberl, 1994) a more difficult problem has been addressed recently: namely, the prediction of building energy consumption well into the future without knowledge of immediately past energy consumption. This paper will report results on a recent study of six months of hourly data recorded at the Zachry Engineering Center (ZEC) in College Station, TX. Also reported are results on finding the R and C values for buildings from networks trained on building data.

Experience has shown that buildings on average may consume 20% more energy than required for occupant comfort which by one estimate leads to $18 billion wasted annually on energy costs in commercial buildings in the United States. Experience and large scale studies of the benefits of commissioning have shown the effectiveness of these services in improving the energy efficiency of commercial buildings. While commissioning services do help reduce energy consumption and improve performance of buildings, the benefits of the commissioning tend to degrade over time. In order to prolong the benefits of commissioning, a prototype fault detection and diagnostic (FDD) tool intended to aid in reducing excess energy consumption known as an Automated Building Commissioning Analysis Tool (ABCAT) has been developed. ABCAT is a first principles based whole building level top down FDD tool which does not require the level of expertise and money often associated with more detailed component level methods. The model based ABCAT tool uses the ASHRAE Simplified Energy Analysis Procedure (SEAP) which requires a smaller number of inputs than more sophisticated simulation methods such as EnergyPlus or DOE-2. ABCAT utilizes a calibrated mathematical model, white box method, to predict energy consumption for given weather conditions. A detailed description of the methodology is presented along with test application results from more than 20 building years worth of retrospective applications and greater than five building years worth of live test case applications. In this testing, the ABCAT tool was used to successfully identify 24 significant energy consumption deviations in five retrospective applications and five significant energy consumption deviations in four live applications.

An empirical or regression modeling approach is simple to develop and easy to use compared to detailed hourly simulations of energy use in commercial buildings. Therefore, regression models developed from measured energy data are becoming an increasingly popular method for determining retrofit savings or identifying operational and maintenance (O&M) problems. Because energy consumption in large commercial buildings is a complex function of climatic conditions, building characteristics, building usage, system characteristics and type of heating, ventilation, and air conditioning (HVAC) equipment used, a multiple linear regression (MLR) model provides better accuracy than a single-variable model for modeling energy consumption. Also, when hourly monitored data are available, an issue which arises is what time resolution to adopt for regression models to be most accurate. This paper addresses both these topics. This paper reviews the literature on MLR models of building energy use, describes the methodology to develop MLR models, and highlights the usefulness of MLR models as baseline models and in detecting deviations in energy consumption resulting from major operational changes. The paper first develops the functional basis of cooling energy use for two commonly used HVAC systems: dual-duct constant volume (DDCV) and dual-duct variable air volume (DDVAV). Using these functional forms, the cooling energy consumption in five large commercial buildings located in central Texas were modeled at monthly, daily, hourly, and hour-of-day (HOD) time scales. Compared to the single-variable model (two-parameter model with outdoor dry-bulb as the only variable), MLR models showed a decrease in coefficient of variation (CV) between 10 percent to 60 percent, with an average decrease of about 33 percent, thus clearly indicating the superiority of MLR models. Although the models at the monthly time scale had higher coefficient of determination (R2) and lower CV than daily, hourly, and HOD models, the daily and HOD models proved more accurate at predicting cooling energy use.

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