Juan-Carlos Baltazar

Study of Cubic Splines and Fourier Series as Interpolation Techniques for Filling in Short Periods of Missing Building Energy Use and Weather Data

A study of cubic splines and Fourier series as interpolation techniques for filling in missing data in energy and meteorological time series is presented. The followed procedure created artificially missing points (pseudo-gaps) in measured data sets and was based on the local behavior of the data set around those pseudo-gaps. Five variants of the cubic spline technique and 12 variants of Fourier series were tested and compared with linear interpolation, for filling in gaps of 1 to 6 hours of data in 20 samples of energy use and weather data. Each of the samples is at least one year in length. The analysis showed that linear interpolation is superior to the spline and Fourier series techniques for filling in 1–6 hour gaps in time series dry bulb and dew point temperature data. For filling 1–6 hour gaps in building cooling and heating use, the Fourier series approach with 24 data points before and after each gap and six constants was found to be the most suitable. In cases where there are insufficient data points for the application of this approach, simple linear interpolation is recommended.Copyright

Literature review of building peak cooling load methods in the United States

Today, sizing HVAC systems plays an important role in the building design process, requiring an accurate method in order to avoid problems from over- or under-sized systems. To date, there have been five peak cooling load methods published by ASHRAE, including: the Total Equivalent Temperature Difference/Time Averaging Method, the Transfer Function Method, the Cooling Load Temperature Difference/Solar Cooling Load/Cooling Load Factor Method, the Heat Balance Method, and the Radiant Time Series Method. This study provides a thorough review of the five methods with respect to their history and summarizes the method differences that can lead to inaccurate sensible cooling load calculations.

Comparison of ASHRAE peak cooling load calculation methods

The building peak cooling load calculation is one of the fundamental steps to develop a proper whole-building HVAC system design. The accuracy of the calculation not only impacts the system size but also influences the building’s performance over the long run since oversized or undersized HVAC systems can exhibit less than optimal operation. Therefore, an accurate, easy-to-use method has been a goal for more than 60 years in the United States. To date, ASHRAE has published five methods for determining building peak cooling loads, including the total equivalent temperature difference/time averaging (TETD/TA) method, the transfer function method (TFM), the cooling load temperature difference/solar cooling load/cooling load factor (CLTD/SCL/CLF) method, the heat balance method (HBM), and the radiant time series method (RTSM). This article presents the results of a comprehensive comparison of these building peak cooling load calculation methods with a focus on the sensible building envelope cooling loads. The results show that the HBM is the most accurate method, followed by the RTSM, the TFM, the TETD/TA method, and the CLTD/SCL/CLF method.

Methodology to Develop the Airport Terminal Building Energy Use Intensity (ATB-EUI) Benchmarking Tool

The report outlines the overall data collection and analysis process for developing airport terminal building energy use intensity (ATB-EUI) benchmarks. Individual chapters highlight: defining annual EUI per ATB zone; annual EU calculation per ATB system; estimation of ATB’s measured annual EU based on utility information; the complete ATB annual EU/EUI table; the input form; and site visits. The report complements CD-ROM 178: Airport Terminal Building Energy Use Intensity (ATB-EUI) Benchmarking Tool.

Methodology for adjusting residential cooling and heating seasonal performance ratings to exclude supply fan energy

This article presents new models to convert the rated cooling and heating seasonal performance efficiency (i.e., SEER or heating seasonal performance factor) to steady-state efficiency rating (i.e., energy efficiency ratio or coefficient of performance) that do not include supply fan energy to be used in building energy simulations for the units less than 19,000 W (65,000 Btu/hr). A review of the two existing conversion equations found that the existing methods do not adequately reflect the characteristics of the units currently available on the market (i.e., units higher than SEER 13/heating seasonal performance factor 7.7) that comply with the provision of the National Appliance Energy Conservation Act of 2006. This analysis was performed using the two new datasets from the California Energy Commission database and the 2012 AHRI directory as well as the AHRI fan performance data collected from several manufacturers. The developed models were adopted in the new edition of ASHRAE Standard 90.1-2013: Energy Cost Budget Method (Section 11) and Performance Rating Method (Appendix G), which is expected to be used in building energy simulations, especially at the design stage. These improved models will allow a more accurate calculation of the impact of the HVAC system efficiency on building energy use compared to the two existing conversion equations, which are discussed in this article. The impact of using the new models on building energy simulation was studied using a 2009 IECC code-compliant, 232-m2 (2500-ft2) house varying the air conditioners SEER and heating seasonal performance factor ratings.