Energy Consumption Analysis Using Measured Data from a Net-Zero Energy Commercial Building in a Cold and Dry Climate

Zero-energy buildings have a critical role in reducing global energy use and greenhouse gas emissions. However, few studies have analyzed net-zero energy commercial buildings using measured energy use such as whole-building level and end-use level data. This paper presents an energy consumption analysis for the first net-zero energy commercial building in Idaho, U.S., in a cold and dry climate using measured end-use data from this building as well as measured whole-building energy use. Monthly bill data analysis, end-use data analysis, and Energy Use Intensity (EUI) analysis were conducted. The combined analysis of this study shows that the HVAC system was the most sensitive to the outside air temperature, showing different energy use percentages of 48.4%, 35.1% (the heating period), 21.6% (the weather-independent period), and 33.4% (the cooling period), respectively. Lighting had the highest percentage of 35.2% for the weather-independent period. The PV electricity generation was higher than the building electricity use, except from December 2017 to February 2018, and the building was net-positive from an energy perspective. The calculated EUI of the building was 34.2 kWh/m2·y, which can be compared to the EUIs of other net zero energy buildings. The approaches developed in this study can be useful for analyzing several net zero buildings by different weather profiles

Large Scale Energy Signature Analysis: Tools for Utility Managers and Planners

Building energy signature analysis is a well-established tool for understanding the temperature sensitivity of building energy consumption and measuring energy savings. This tool has been used to measure energy savings of residential, commercial, and even industrial buildings. The public availability of electricity loads (i.e., hourly electricity demand (MW)) from entire Balancing Authorities (BAs) provide an interesting opportunity to apply this approach to a large aggregate load. In this paper, we explore that opportunity for BAs and show that the correlations for large geographical areas are surprisingly coherent when the change-point linear regression analysis is used with the daily interval data of electricity demand and outside air temperature. The change-point linear regression models of all the BAs, except WAUW and OVEC, show R2 of 0.70 or more and CV-RMSE of 10.0% or less. We also suggest an analysis method that allows for meaningful comparisons between BAs and to assess changes in time for a given BA which could be used to interpret changes in load patterns year-to-year, accounting for changes in weather. This approach can be used to verify the impact of energy efficiency programs on a building component/system-wide basis. This study shows the annual electricity demand reductions for SCL and IPCO are 136,655 MWh (1.5%) and 182,053 MWh (1.1%), respectively

Statewide Electricity and Demand Capacity Savings From the International Energy Conservation Code (IECC) Adoption for Single-Family Residences in Texas (2002-2013)

This report present the statewide electricity and electric demand savings achieved from the adoption of the different International Energy Conservation Code (IECC) versions for single-family residences in Texas and the corresponding construction cost increases over the twelve-year period from 2002 through 2013. Using the Energy Systems Laboratory’s International Code Compliance Calculator (IC3) simulation program, the annual electricity savings in 2013 are estimated to be $168 million, and the demand reductions in 2013 are estimated to be 1,166 MW for the summer and 1,175 MW for the winter periods. Since 2002, the cumulative statewide electricity and electric demand savings over the twelveyear period from 2002 to 2013 are$2,966 million for the summer ($1,403 million from electricity savings and$1,563 million from demand savings) and $2,977 million for the winter periods ($1,403 million from electricity savings and $1,574 million from demand savings), while the total implementation costs are estimated to be$1,060 million

Statewide Air Emissions Calculations from Wind and Other Renewables: Summary Report

The 79th Legislature, through Senate Bill 20, House Bill 2481 and House Bill 2129, amended Senate Bill 5 to enhance its effectiveness by adding 5,880 MW of generating capacity from renewable energy technologies by 2015 and 500 MW from non-wind renewables. This legislation also requires the Public Utilities Commission of Texas (PUCT) to establish a target of 10,000 megawatts of installed renewable capacity by 2025, and requires the Texas Commission on Environmental Quality (TCEQ) to develop methodology for computing emissions reductions from renewable energy initiatives and the associated credits. Table 1-1 lists the statutory mandates and total wind power generation capacity (including installed and announced) in Texas from 2001 to 2025. It shows that Texas has achieved its milestone of 10,000 MW by the end of 2010 and could reach 24,561 MW by 2017 according to the information from PUCT1. In this Legislation, the function of the Energy Systems Laboratory (ESL) is to assist the TCEQ in quantifying emissions reductions credits from energy efficiency and renewable energy programs, through a contract with the Texas Environmental Research Consortium (TERC) to develop and annually calculate creditable emissions reductions from wind and other renewable energy resources for the State Implementation Plan (SIP). The Energy Systems Laboratory, in fulfillment of its responsibilities under this Legislation, submits its tenth annual report, “Statewide Air Emissions Calculations from Wind and Other Renewables,” to the Texas Commission on Environmental Quality

Detailed Analysis of Thermal Comfort and Indoor Air Quality Using Real-Time Multiple Environmental Monitoring Data for a Childcare Center

Thermal comfort, indoor air quality (IAQ), and energy use are closely related, even though these have different aspects with respect to building performance. We analyzed thermal comfort and IAQ using real-time multiple environmental data, which include indoor air temperature, relative humidity, carbon dioxide (CO2), and particulate matter (e.g., PM10 and PM2.5), as well as electricity use from an energy recovery ventilation (ERV) system for a childcare center. Thermal comfort frequency and time-series analyses were conducted in detail to thoroughly observe real-time thermal comfort and IAQ conditions with and without ERV operation, and to identify energy savings opportunities during occupied and unoccupied hours. The results show that the highest CO2 and PM10 concentrations were reduced by 51.4% and 29.5%, respectively, during the occupied hours when the ERV system was operating. However, it was also identified that comfort frequencies occurred during unoccupied hours and discomfort frequencies during occupied hours. By analyzing and communicating the three different types of real-time monitoring data, it is concluded that the ERV system should be controlled by considering not only IAQ (e.g., CO2 and PM2.5) but also thermal comfort and energy use to enhance indoor environmental quality and save energy based on real-time multiple monitoring data

Large Scale Energy Signature Analysis: Tools for Utility Managers and Planners

Building energy signature analysis is a well-established tool for understanding the temperature sensitivity of building energy consumption and measuring energy savings. This tool has been used to measure energy savings of residential, commercial, and even industrial buildings. The public availability of electricity loads (i.e., hourly electricity demand (MW)) from entire Balancing Authorities (BAs) provide an interesting opportunity to apply this approach to a large aggregate load. In this paper, we explore that opportunity for BAs and show that the correlations for large geographical areas are surprisingly coherent when the change-point linear regression analysis is used with the daily interval data of electricity demand and outside air temperature. The change-point linear regression models of all the BAs, except WAUW and OVEC, show R2 of 0.70 or more and CV-RMSE of 10.0% or less. We also suggest an analysis method that allows for meaningful comparisons between BAs and to assess changes in time for a given BA which could be used to interpret changes in load patterns year-to-year, accounting for changes in weather. This approach can be used to verify the impact of energy efficiency programs on a building component/system-wide basis. This study shows the annual electricity demand reductions for SCL and IPCO are 136,655 MWh (1.5%) and 182,053 MWh (1.1%), respectively

Change-Point Modeling Analysis for Multi-Residential Buildings: A Case Study in South Korea

Residential energy use data has become more readily available through the Advanced Metering Infrastructure (AMI). AMI data can have greater impacts in various fields that employ building energy analysis such as energy use prediction, fault detection, model calibration, and short-term monitoring because the AMI data is more meaningful due to granularity. However, this is not always true if we do not properly use the AMI data. In this paper, we evaluated how interval energy use data is useful for the prediction and short-term monitoring for residential buildings. Two phases were applied to multi-residential buildings in a case study apartment in South Korea. Phase I compares the change-point linear regression models between daily, weekly, and monthly interval energy use data. Phase II determines the minimum data period required to determine each coefficient of change-point linear regression models using an advanced analysis method compared to a previous Dry-Bulb Temperature Analysis (DBTA) study. The results from Phase I showed that weekly interval data could be the best option to analyze residential energy use. Phase II demonstrated that the new analysis method, called the coefficient checking method, is useful to find short-term energy monitoring periods for the data logger installation in terms of weather-independent and weather-dependent electricity use as well as the prediction of the whole-building energy use

Drone-Assisted Image Processing Scheme using Frame-Based Location Identification for Crack and Energy Loss Detection in Building Envelopes

This paper presents improved methods to detect cracks and thermal leakage in building envelopes using unmanned aerial vehicles (UAV) (i.e., drones) with video camcorders and/or infrared cameras. Three widely used contour detectors of Sobel, Laplacian, and Canny algorithms were compared to find a better solution with low computational overhead. Furthermore, a scheme using frame-based location identification was developed to effectively utilize the existing approach by finding the current location of the drone-assisted image frame. The results showed a simplified drone-assisted scheme along with automation, higher accuracy, and better speed while using lower battery energy. Furthermore, this paper found that the cost-effective drone with the attached equipment generated accurate results without using an expensive drone. The new scheme of this paper will contribute to automated anomaly detection, energy auditing, and commissioning for sustainably built environments