D. E. Claridge

Ambient-temperature regression analysis for estimating retrofit savings in commercial buildings

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.

Building Energy Use Prediction and System Identification Using Recurrent Neural Networks

Following several successful applications of feedforward neural networks (NNs) to the building energy prediction problem 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.

A four-parameter change-point model for predicting energy consumption in commercial buildings

This paper develops a four-parameter change-point model of energy consumption as a function of dry-bulb temperature, along with accompanying error diagnostics for the models parameters. The model is a generalization of the widely used three-parameter, or variable-base degree-day method. The model is applied to data from a case study grocery store, is compared to the three-parameter PRISM CO model of the store data, and is shown to provide a statistically better fit to consumption data below about 15{degrees}C. This model appears to be useful for diagnosing unexpected energy use in some buildings and should be useful for determining retrofit energy savings from monitored pre-retrofit and post-retrofit data for the class of buildings whose pre-retrofit consumption is fit by a four-parameter linear change-point model.

ITPE technique applications to time-varying two-dimensional ground-coupling problems

Abstract The interzone temperature profile estimation (ITPE) procedure is used to find two-dimensional analytical series solutions for the time-varying heat transfer between ground and slab-on-grade floors or basements. The undisturbed soil temperature is approximated as a sinusoidal function of time and the ITPE procedure is coupled with the complex temperature technique to derive the steady-periodic solutions for both configurations. The influence of insulation and of a fixed-temperature water table on the temporal behavior of slab-on-grade floors and basements is treated analytically for the first time.

Analytical Model to Predict Annual Soil Surface Temperature Variation

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.

A Fourier series model to predict hourly heating and cooling energy use in commercial buildings with outdoor temperature as the only weather variable

Accurate modeling of hourly heating and cooling energy use in commercial buildings can be achieved by a Generalized Fourier Series (GFS) approach involving weather variables such as dry-bulb temperature, specific humidity and horizontal solar flux. However, there are situations when only temperature data is available. The objective of this paper is to (i) describe development of a variant of the GFS approach which allows modeling both heating and cooling hourly energy use in commercial buildings with outdoor temperature as the only weather variable and (ii) illustrate its application with monitored hourly data from several buildings in Texas. It is found that the new Temperature based Fourier Series (TFS) approach (1) provides better approximation to heating energy use than the existing GFS approach, (ii) can indirectly account for humidity and solar effects in the cooling energy use, (iii) offers physical insight into the operating pattern of a building HVAC system and (iv) can be used for diagnostic purposes.

Use of calibrated HVAC system models to optimize system operation

A calibrated simplified engineering modeling method has been developed to optimize HVAC system operation. This method can be used to optimize operating strategies and control schedules. It has also served to identify malfunctioning components on occasion. This method has been successfully applied to 18 LoanSTAR buildings where over

Using synthetic data to evaluate multiple regression and principal component analyses for statistical modeling of daily building energy consumption

2.0 million per year in potential savings have been identified, and over

Modeling Hourly Energy Use in Commercial Buildings With Fourier Series Functional Forms

1.4 million per year have been implemented. The method is described in the context of a case study.

Is Commissioning Once Enough

Abstract Multiple regression modeling of monitored building energy use data is often faulted as a reliable means of predicting energy use on the grounds that multicollinearity between the regressor variables can lead both to improper interpretation of the relative importance of the various physical regressor parameters and to a model with unstable regressor coefficients. Principal component analysis (PCA) has the potential to overcome such drawbacks. While a few case studies have already attempted to apply this technique to building energy data, the objectives of this study were to make a broader evaluation of PCA and multiple regression analysis (MRA) and to establish guidelines under which one approach is preferable to the other. Four geographic locations in the US with different climatic conditions were selected and synthetic data sequences representative of daily energy use in large institutional buildings were generated in each location using a linear model with outdoor temperature, outdoor specific humidity and solar radiation as the three regression variables. MRA and PCA approaches were then applied to these data sets and their relative performances were compared. Conditions under which PCA seems to perform better than MRA were identified and preliminary recommendations on the use of either modeling approach formulated.