• Energy Research

Pursuing innovations in sustainable architectural solutions, this study examines the impact of a climate-adaptive building envelope with dynamic photovoltaic integrated shading devices (PVSDs) on building performance. A major challenge in designing PVSDs is the lack of established guidelines for geometry and operations. We delve into the complexities and potential benefits of integrating dynamic PVSD designs into building performance simulations, particularly considering their time-varying geometric and operational aspects. This research assesses a range of similar PVSD design options with differing patterns, emphasizing their effects on solar energy potential, daylighting, and thermal efficiency. We conducted tests on south-oriented PVSDs (featuring two-axis rotation) in Houston, Texas, focusing on variables such as panel count (4 or 36), rotation angle range, and operational patterns (synchronized or individual). Regarding solar potential, the four-panel synchronized PVSD option outperformed static shading by 2.1 times. For daylighting and thermal performance, the 36-panel synchronized option with a wide rotation range and the four-panel individual option proved superior to other PVSD configurations, improving up to an average of 36% (sDA300/50%) and 1.5 °C, respectively. Our findings emphasize the critical role of integrating geometric design and operational patterns in PVSDs for enhanced system effectiveness and highlight PVSD design and application limitations. Our findings emphasize the critical role of integrating geometric design and operational patterns in PVSDs for enhanced system effectiveness. Furthermore, they shed light on the limitations in the PVSD design process and practical applications.

Abstract 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.

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.

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.

Abstract Many utility companies have installed Smart Meters (SMs) for residential and commercial buildings in the U.S., which are the part of the Smart Grid (SG) that integrates the electricity grid with communication networks. Along with the growing interest in SMs, the development of the wireless technologies and smart phones has accelerated the applications of Home Automation Devices (HADs) that can also communicate with SMs, Home Energy Management Systems (HEMS), and smart phones. However, there are few if any previous studies that analyze the potential energy saving opportunities for homeowners from HADs using interval data recorded by SMs. Therefore, this paper presents five new pre-screening analysis methods that use interval energy consumption data to better characterize building energy use for the residential customers who want energy savings from the use of HADs before they are installed. This paper is part of a larger study that analyzed and measured energy savings from the use of HADs with smart meter data.

Many smart apartments and renovated residential buildings have installed Smart Meters (SMs), which collect interval data to accelerate more efficient energy management in multi-family residential buildings. SMs are widely used for electricity, but many utility companies have been working on systems for natural gas and water monitoring to be included in SMs. In this study, we analyze heating energy use data obtained from SMs for short-term monitoring and annual predictions using change-point models for the coefficient checking method. It was found that 9-month periods were required to search the best short-term heating energy monitoring periods when non-weather-related and weather-related heating loads and heating change-point temperatures are considered. In addition, the 9-month to 11-month periods were needed for the analysis to apply to other case study residences in the same high-rise apartment. For the accurate annual heating prediction, 11-month periods were necessary. Finally, the results from the heating performance analysis of this study were compared with the cooling performance analysis from a previous study. This study found that the coefficient checking method is a simple and easy-to-interpret approach to analyze interval heating energy use in multi-family residential buildings. It was also found that the period of short-term energy monitoring should be carefully selected to effectively collect targeted heating and cooling data for an energy audit or annual prediction.

Building energy simulation models have been used to assist the design and/or optimization of buildings energy performance. The results from building energy simulation models can be more reliable when measured energy use data, indoor environmental condition data, system operation status, and coincident weather data are used to validate the simulation results. In this paper, given the wide-spread use of home automation devices in residential buildings, we studied how well a residential building energy simulation model can be tuned using measured interval data from a smart thermostat and smart meter. The analysis is based on a multi-stage approach that can help improve the reliability of the use of building energy simulation models that reflect both the indoor air temperature and whole-building energy use. Results from changing the input parameters in the building simulation show that the comparison of the simulated and measured indoor temperatures fall in a range below a NMBE of 1.5% and a CV-RMSE of 2.2%, while the simulated whole-building energy use matches the measured energy use below a NMBE of −2.7% and a CV-RMSE of 10.9%. We found that the most significant parameters for the indoor air temperature and whole-building energy use were the effective U-value for the slab-on-grade floor and the heating and cooling system operation status, respectively.

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.