Energy Use In Commercial Buildings Research Articles

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 (i) 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.

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

Hourly energy use in commercial buildings shows periodic variation in daily and annual cycles. Moreover, the pattern of variation is such that Fourier series functional forms provide one of the best approaches for modeling this behavior. Results from numerous case studies have demonstrated the power of Fourier series as a modeling tool to represent hourly energy use in commercial buildings. This paper describes the Fourier series approaches that have been developed for modeling energy use in commercial buildings and presents the results of application to data collected from many sites.

Uncertainty in Baseline Regression Modeling and in Determination of Retrofit Savings

The objective of this paper is to discuss the various sources of uncertainty inherent in the estimation of actual measured energy savings from baseline regression models, and to present pertinent statistical concepts and formulae to determine this uncertainty. Regression models of energy use in commercial buildings are not of the “standard” type addressed in textbooks because of the changepoint behavior of the models and the effect of patterned and non-constant variance residuals (largely as a result of changes in operating modes of the building and the HVAC system). This paper also addresses such issues as how model prediction is impacted by both improper model residuals and models identified from data periods which do not encompass the entire range of variation of both climatic conditions and the different building operating modes.

Multivariate Regression Modeling

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.

A Perspective on Methods for Analysis of Measured Energy Data from Commercial Buildings

This paper provides a historical perspective on the methods used to analyze measured energy use in commercial buildings. It summarizes the capabilities and uncertainties of the regression methods used in most M&V applications today and the calibrated simulation approaches used for M&V, commissioning, and end-use disaggregation. The need for graphical indices is introduced and applications of artificial neural networks, Fourier series and spectral analysis methods for M&V and data acquisition are described.

Effects of Lighting, Zoning, and Control Strategies on Energy Use in Commercial Buildings

Effects of Lighting, Zoning, and Control Strategies on Energy Use in Commercial Buildings

Determinants of Energy Use in Institutional Buildings: A Minnesota Example

Energy use data are usually disaggregated by major end-use sector: residential, commercial, industrial, transportation. Generally speak- ing, data are weakest for the commercial sector, perhaps because this sector is often defined as a residual (i.e., that portion of the economy not included in the other sectors). However, energy use in commercial buildings accounts for about 15 percent of total U.S. energy use and is grow- ing more rapidly than energy use in other sectors. For example, commercial energy use amounted to almost 10 QBtu (10 EJ) in 1979; the average growth rate in commercial sector energy use was 1 percent per year between 1973 and 1979, compared with a growth rate of 0.3 percent per year for total U.S. energy use [1; 8].