Automated pipeline framework for processing of large-scale building energy time series data.

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for "virtual" energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings' time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building's daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings.

Current approaches to building efficiency diagnoses include conventional energy audit techniques that can be expensive and time consuming. In contrast, virtual energy audits of readily available 15-minute-interval building electricity consumption are being explored to provide quick, inexpensive, and useful insights into building operation characteristics. A cross sectional analysis of six buildings in two different climate zones provides methods for data cleaning, population-based building comparisons, and relationships (correlations) of weather and electricity consumption. Data cleaning methods have been developed to categorize and appropriately filter or correct anomalous data including outliers, missing data, and erroneous values (resulting in < 0.5% anomalies). The utility of a cross-sectional analysis of a sample set of building's electricity consumption is found through comparisons of baseload, daily consumption variance, and energy use intensity. Correlations of weather and electricity consumption 15-minute interval datasets show important relationships for the heating and cooling seasons using computed correlations of a Time-Specific-Averaged-Ordered Variable (exterior temperature) and corresponding averaged variables (electricity consumption)(TSAOV method). The TSAOV method is unique as it introduces time of day as a third variable while also minimizing randomness in both correlated variables through averaging. This study found that many of the pair-wise linear correlation analyses lacked strong relationships, prompting the development of the new TSAOV method to uncover the causal relationship between electricity and weather. We conclude that a combination of varied HVAC system operations, building thermal mass, plug load use, and building set point temperatures are likely responsible for the poor correlations in the prior studies, while the correlation of time-specific-averaged-ordered temperature and corresponding averaged variables method developed herein adequately accounts for these issues and enables discovery of strong linear pair-wise correlation R values. TSAOV correlations lay the foundation for a new approach to building studies, that mitigates plug load interferences and identifies more accurate insights into weather-energy relationship for all building types. Over all six buildings analyzed the TSAOV method reported very significant average correlations per building of 0.94 to 0.82 in magnitude. Our rigorous statistics-based methods applied to 15-minute-interval electricity data further enables virtual energy audits of buildings to quickly and inexpensively inform energy savings measures.

Microinverter Thermal Performance in the Real-World: Measurements and Modeling.

Real-world performance, durability and reliability of microinverters are critical concerns for microinverter-equipped photovoltaic systems. We conducted a data-driven study of the thermal performance of 24 new microinverters (Enphase M215) connected to 8 different brands of PV modules on dual-axis trackers at the Solar Durability and Lifetime Extension (SDLE) SunFarm at Case Western Reserve University, based on minute by minute power and thermal data from the microinverters and PV modules along with insolation and environmental data from July through October 2013. The analysis shows the strengths of the associations of microinverter temperature with ambient temperature, PV module temperature, irradiance and AC power of the PV systems. The importance of the covariates are rank ordered. A multiple regression model was developed and tested based on stable solar noon-time data, which gives both an overall function that predicts the temperature of microinverters under typical local conditions, and coefficients adjustments reecting refined prediction of the microinverter temperature connected to the 8 brands of PV modules in the study. The model allows for prediction of internal temperature for the Enphase M215 given similar climatic condition and can be expanded to predict microinverter temperature in fixed-rack and roof-top PV systems. This study is foundational in that similar models built on later stage data in the life of a device could reveal potential influencing factors in performance degradation.

The thermal flash technique: the inconsequential effect of contact resistance and the characterization of carbon nanotube clusters.

This article presents a comprehensive mathematical treatment of the theory behind the thermal flash technique used to measure the thermal diffusivity of nanostructures. Analytical expressions predicting the temperature and its rate of change for various combinations of sample length and diffusivity confirmed that the presence of contact resistance between the heat sink/source or within a cluster of materials does not influence the measurement. Measurements on multi-walled carbon nanotube clusters provide further experimental evidence supporting the claim that contact resistance is inconsequential to this technique and yield a thermal conductivity of 2665 W/m K, which corresponds to an isolated nanotube and not the overall cluster.

Application of the thermal flash technique for low thermal diffusivity micro/nanofibers.

The thermal flash method was developed to characterize the thermal diffusivity of micro/nanofibers without concern for thermal contact resistance, which is commonly a barrier to accurate thermal measurement of these materials. Within a scanning electron microscope, a micromanipulator supplies instantaneous heating to the micro/nanofiber, and the resulting transient thermal response is detected at a microfabricated silicon sensor. These data are used to determine thermal diffusivity. Glass fibers of diameter 15 microm had a measured diffusivity of 1.21x10(-7) m(2)/s; polyimide fibers of diameters 570 and 271 nm exhibited diffusivities of 5.97x10(-8) and 6.28x10(-8) m(2)/s, respectively, which compare favorably with bulk values.

The numerical mirage method for photothermal characterization of materials.

Noncontact thermal measurement techniques offer rapid thermal characterization without modification or destruction of the sample being studied. A simple and versatile method has been developed, termed the "numerical mirage method," that utilizes the transient photothermal deflection of a laser beam traversing a modulated temperature gradient. This method expands the range and simplifies the experimental procedure of traditional mirage methods. A numerical solver is used to create accurate deflection profile models and a linear curve fitting routine is developed, from which the thermal diffusivity of a material may be determined. This method allows for rapid modification of sample and heating configurations. Verification of the method is performed on bismuth and fused quartz reference samples, and good agreement with literature is obtained.