Energy Systems Laboratory: Building a Model Repository Collection

The Energy Systems Laboratory (ESL) is a division of the Texas Engineering Experiment Station and part of the Texas A&M University System. First established in 1939, the ESL maintains a testing laboratory on the Riverside Campus in Bryan, Texas, and offices on the main campus of Texas A&M. The group consists of five faculty members from the Department of Mechanical Engineering, as well as three faculty members from the Departments of Architecture and Construction Science. The lab currently employs approximately 120 staff members, including mechanical engineers, computer science graduates, lab technicians, support staff, and graduate and undergraduate students. The Lab focuses on energy-related research, energy efficiency, and emissions reduction, and has a total annual income for external research and testing exceeding $4.5 million. With energy research and policy at the forefront of public discussion, both academic and political, the urgency of making this research publicly available is very high. The Energy Systems Laboratory collection in the Texas A&M Digital Repository is unique in a number of ways. After first contacting the library in March 2005, the ESL became one of Texas A&M's earliest adopters of the repository. The collection is very diverse, and contains conference proceedings, published articles, technical reports, and electronic theses and dissertations produced by students affiliated with the ESL. The ESL is also the first repository client to take the initiative of assigning staff members to learn the batch loading process for themselves, both relieving library staff of the burden and allowing the collection to expand even more rapidly. The collection has also successfully made the transition, despite some challenges, from the original DSpace interface to the Manakin-themed repository now in place. After three years, the collection remains one of the largest collections in the system, continues to grow as more of the group's research and publications are added to the collection, and is held forth as a model collection to prospective repository clients in the Texas A&M community. This is a testament to the Energy Systems Laboratory's dedication to the building of their repository collection, and their clear understanding of the advantages of open access. This presentation will discuss the excellent working relationship built between the Energy System Laboratory and the library, and how such relationships can be fostered with other collections as the repository expands. It will also recount the events leading up to the ESL's original adoption of the repository, and will chronicle the evolution of the repository collection, the addition of new content, the transition and adaptation to new technology, the copyright and other challenges faced, and the group's future needs for additional tools and services

Regional Energy Baselines and Measurement and Verification Protocols: Subtask 3.1 for the Southern Energy Efficiency Center

The Southern Energy Efficiency Center (SEEC) was established to substantially increase the deployment of high-performance “beyond-code” buildings across the southern region of the U.S. It is funded by the U.S. Department of Energy (DOE) Building Technologies Program, and administered by the National Energy Technology Laboratory. The goal of the first 18-month phase was to address efficiency goals of states, utilities, and various energy-efficiency programs. In order to achieve this goal, the project efforts included defining the baseline energy patterns within the project region, as well as the measurement and verification (M&V) protocols for use in determining the efficiency improvements SEEC, state and USDOE efforts with respect to that baseline. This work is defined under the SEEC Subtask 3.1 Define Regional Baselines and Measurement & Verification Protocols. This report presents preliminary deliverables of this subtask developed and documented by the Energy Systems Laboratory (ESL) for use by the SEEC member state region. The primary goal of this subtask is to provide the state energy offices with a comparison tool of energy use either by total or per-capita. This tool is expected to allow the state energy offices to compare their energy use pattern against other states’ and the national average energy use by end-use sector. In addition, they can use this tool for a comparison of energy use within their states by end-use and by fuel-source. Another goal of this subtask is to demonstrate the usability of public-available data such as the U.S. Department of Energy’s Energy Information Agency (DOE EIA) data sets and the U.S. Census Bureau data sets. This approach has been successfully demonstrated by ESL as part of the Comptroller of Public Accounts and the State Energy Conservation Office report on Texas Energy Future. Limited preliminary analysis of the data was made since it was not a project goal. The data provides the basis by which extensive state by state analysis can begin. In addition, the recommended measurement and verification (M&V) protocols for an individual building or facility, ASHRAE/CIBSE/USGBC Performance Measurement Protocols (PMP) for Commercial Buildings, can be used as a bottom-up approach for energy efficiency improvements of buildings within the SEEC 12-state region

Review and Recommendations of Existing Methods and Tools for Building Energy Analysis: Subtask 2.4 for the Southern Energy Efficiency Center

ESL-TR-09-04-01The Southern Energy Efficiency Center (SEEC) was established to substantially increase the deployment of high-performance “beyond-code” buildings across the southern region of the U.S. It is funded by the U.S. Department of Energy (DOE) Building Technologies Program and administered by the National Energy Technology Laboratory. During its first 18-month phase, to expand the use of existing methods, procedures and tools for building energy efficiency in the marketplace; project efforts include identifying the existing tools from very simple calculators for estimating energy savings to detailed methods for measurement and verification of commercial building energy savings and defining their technical and practical characteristics. This work is defined under the SEEC Subtask 2.4 Expand the Use of Existing Methods and Tools. This report presents preliminary deliverables of this subtask developed and documented by the Energy Systems Laboratory (ESL) for use by the SEEC member state region. The primary goal of this subtask is to provide the state energy offices with the list of available tools and recommendations for use. By scrutinizing the information gathered, these recommendations have been developed to encourage the use of a number of existing tools that are not widely used, but provide valuable information and insight on the benefits of building energy efficiency in the SEEC member states. The resultant summary spreadsheet will also allow them to choose the appropriate tool, either simple calculators or detailed methods, according to the inquiry

Translating Building Information Modeling to Building Energy Modeling Using Model View Definition

This paper presents a new approach to translate between Building Information Modeling (BIM) and Building Energy Modeling (BEM) that uses Modelica, an object-oriented declarative, equation-based simulation environment. The approach (BIM2BEM) has been developed using a data modeling method to enable seamless model translations of building geometry, materials, and topology. Using data modeling, we created a Model View Definition (MVD) consisting of a process model and a class diagram. The process model demonstrates object-mapping between BIM and Modelica-based BEM (ModelicaBEM) and facilitates the definition of required information during model translations. The class diagram represents the information and object relationships to produce a class package intermediate between the BIM and BEM. The implementation of the intermediate class package enables system interface (Revit2Modelica) development for automatic BIM data translation into ModelicaBEM. In order to demonstrate and validate our approach, simulation result comparisons have been conducted via three test cases using (1) the BIM-based Modelica models generated from Revit2Modelica and (2) BEM models manually created using LBNL Modelica Buildings library. Our implementation shows that BIM2BEM (1) enables BIM models to be translated into ModelicaBEM models, (2) enables system interface development based on the MVD for thermal simulation, and (3) facilitates the reuse of original BIM data into building energy simulation without an import/export process

Analysis of Above-Code (2009 IECC) Residential Energy Efficiency Measures in ONCOR Service Area

The purpose of this report is to provide an analysis of residential energy efficiency and renewable measures that would exceed the 2009 edition of the International Energy Conservation Code (IECC) in the ONCOR service territory. This information is useful to homebuilders, utility demand side energy managers, homeowners and others who wish to construct buildings that exceed the minimum national energy code requirements. A total of 17 measures based on the energy savings above the base-case house were selected. These measures include Renewable Power Options, Heating Ventilation and Air Conditioning (HVAC), Fenestration, Envelope, Lighting and Domestic Hot Water (DHW) options. Individual measures were then categorized into four groups: 0 to 5%, 5 to 10%, and 10 to 15% and above 15% source energy savings above the base-case house. After categorizing, three example groups were formed combining the individual measures so that the combined source energy savings of the group is 15% above the base-case 2009 code-compliant house. The savings achieved by each group ranged from 15 to 28%. The photovoltaic options presented the most savings in the range of 12-42% for all base-case houses. The analysis was performed using an ESL simulation model based on the DOE-2.1e simulation of a 2009 IECC code-compliant, single-family residence. Two sets of simulations based on the choice of heating fuel type were considered: (a) an air-conditioned house with natural-gas heating/domestic water heating (i.e., gas-fired furnace for space heating and gas water heater for domestic water heating), and (b) an air-conditioned house with electric heating/domestic water heating (i.e., heat pump for space heating and electric water heater for domestic water heating). Version 3.03.02 of the Energy Systems Laboratory’s International Code Compliance Calculator (IC3) was used with the appropriate TMY2 weather files. Different counties in the ONCOR territory were grouped according to 2009 IECC Climate Zone; and finally, two zones—Climate Zone 2 and 3—were identified and analyzed

Function Based Flow Modeling and Animation

This paper summarizes a function-based approach to model and animate 2D and 3D flows. We use periodic functions to create cyclical animations that represent 2D and 3D flows. These periodic functions are constructed with an extremely simple algorithm from a set of oriented lines. The speed and orientation of the flow are described directly by the orientation and the lengths of these oriented lines. The resulting cyclical animations are then obtained by sampling the constructed periodic functions. Our approach is independent of dimension, i.e. for 2D and 3D flow the same types of periodic functions are used. Rendering images for 2D and 3D flows are slightly different. In 2D function values directly are mapped to color values. On the other hand, in 3D function values first mapped to color and opacity and then the volume is rendered by our volume renderer. Modeled and animated flows are used to improve the visualization of operations of rolling piston and rotary vane compressors

Analysis of Zone-by-Zone Indoor Environmental Conditions and Electricity Savings from the Use of a Smart Thermostat: A Residential Case Study

Smart thermostats are becoming an important tool that saves Heating, Ventilating, and Air Conditioning (HVAC) system energy use by optimizing thermostat settings. This paper presents the results of an analysis of measured, zone-by-zone indoor environmental conditions and electricity savings from the use of a smart thermostat that includes temperature and occupancy data from each zone in a single-family residence. In this analysis, statistical indoor air temperature profiles were developed for each zone before and after the installation of the smart thermostat. The analysis shows that the temperature and occupancy-based control of the system produced significant changes to the indoor air temperature profiles in each zone. Although these indoor condition changes were acceptable to the homeowner of the case-study residence, the changes to the before-after indoor air temperature profiles also present new challenges to simulating the annual savings with a calibrated building energy simulation program. The results also show that a residence with a single-zone HVAC system controlled by a single thermostat that was retrofitted with wireless occupancy and temperature sensors in each zone achieved significant electricity savings for the homeowner, as well as electric demand reductions for the electric utility

Analysis Methods for Characterizing Energy Saving Opportunities from Home Automation Devices Using Smart Meter Data

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