Impacts of climate change on energy consumption and peak demand in buildings: A detailed regional approach
Not Available
Dirks, James A.↗
Engineering topics
Gorrissen, Willy J.
Publications and source records attributed to Gorrissen, Willy J..
Smart Grid Status and Metrics Report Appendices
A smart grid uses digital power control and communication technology to improve the reliability, security, flexibility, and efficiency of the electric system, from large generation through the delivery systems to electricity consumers and a growing number of distributed generation and storage resources. To convey progress made in achieving the vision of a smart grid, this report uses a set of six characteristics derived from the National Energy Technology Laboratory Modern Grid Strategy. The Smart Grid Status and Metrics Report defines and examines 21 metrics that collectively provide insight into the grid’s capacity to embody these characteristics. This appendix presents papers covering each of the 21 metrics identified in Section 2.1 of the Smart Grid Status and Metrics Report. These metric papers were prepared in advance of the main body of the report and collectively form its informational backbone.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Nationwide Buildings Energy Research enabled through an integrated Data Intensive Scientific Workflow and Advanced Analysis Environment
Modern workflow systems enable scientists to run ensemble simulations at unprecedented scales and levels of complexity, allowing them to study system sizes previously impossible to achieve, due to the inherent resource requirements needed for the modeling work. However as a result of these new capabilities the science teams suddenly also face unprecedented data volumes that they are unable to analyze with their existing tools and methodologies in a timely fashion. In this paper we will describe the ongoing development work to create an integrated data intensive scientific workflow and analysis environment that offers researchers the ability to easily create and execute complex simulation studies and provides them with different scalable methods to analyze the resulting data volumes. The integration of simulation and analysis environments is hereby not only a question of ease of use, but supports fundamental functions in the correlated analysis of simulation input, execution details and derived results for multi-variant, complex studies. To this end the team extended and integrated the existing capabilities of the Velo data management and analysis infrastructure, the MeDICi data intensive workflow system and RHIPE the R for Hadoop version of the well-known statistics package, as well as developing a new visual analytics interface for the result exploitation by multi-domain users. The capabilities of the new environment are demonstrated on a use case that focusses on the Pacific Northwest National Laboratory (PNNL) building energy team, showing how they were able to take their previously local scale simulations to a nationwide level by utilizing data intensive computing techniques not only for their modeling work, but also for the subsequent analysis of their modeling results. As part of the PNNL research initiative PRIMA (Platform for Regional Integrated Modeling and Analysis) the team performed an initial 3 year study of building energy demands for the US Eastern Interconnect domain, which they are now planning to extend to predict the demand for the complete century. The initial study raised their data demands from a few GBs to 400GB for the 3year study and expected tens of TBs for the full century.
99 GENERAL AND MISCELLANEOUS↗
Understanding Building Infrastructure and Building Operation through DOE Asset Score Model: Lessons Learned from a Pilot Project
The U.S. Department of Energy (DOE) is developing a national voluntary energy asset score system to help building owners to evaluate the as-built physical characteristics (including building envelope, the mechanical and electrical systems) and overall building energy efficiency, independent of occupancy and operational choices. The energy asset score breaks down building energy use information by simulating building performance under typical operating and occupancy conditions for a given use type. A web-based modeling tool, the energy asset score tool facilitates the implementation of the asset score system. The tool consists of a simplified user interface built on a centralized simulation engine (EnergyPlus). It is intended to reduce both the implementation cost for the users and increase modeling standardization compared with an approach that requires users to build their own energy models. A pilot project with forty-two buildings (consisting mostly offices and schools) was conducted in 2012. This paper reports the findings. Participants were asked to collect a minimum set of building data and enter it into the asset score tool. Participants also provided their utility bills, existing ENERGY STAR scores, and previous energy audit/modeling results if available. The results from the asset score tool were compared with the building energy use data provided by the pilot participants. Three comparisons were performed. First, the actual building energy use, either from the utility bills or via ENERGY STAR Portfolio Manager, was compared with the modeled energy use. It was intended to examine how well the energy asset score represents a building’s system efficiencies, and how well it is correlated to a building’s actual energy consumption. Second, calibrated building energy models (where they exist) were used to examine any discrepancies between the asset score model and the pilot participant buildings’ [known] energy use pattern. This comparison examined the end use breakdowns and more detailed time series data. Third, ASHRAE 90.1 prototype buildings were also used as an industry standard modeling approach to test the accuracy level of the asset score tool. Our analysis showed that the asset score tool, which uses simplified building simulation, could provide results comparable to a more detailed energy model. The buildings’ as-built efficiency can be reflected in the energy asset score. An analysis between the modeled energy use through the asset score tool and the actual energy use from the utility bills can further inform building owners about the effectiveness of their building’s operation and maintenance.
Wang, Na↗
Commercial Building Energy Asset Score System: Program Overview and Technical Protocol (Version 1.0)
The U.S. Department of Energy (DOE) is developing a national voluntary energy asset score system that includes an energy asset score tool to help building owners evaluate their buildings with respect to the score system. The goal of the energy asset score system is to facilitate cost-effective investment in energy efficiency improvements of commercial buildings. The system will allow building owners and managers to compare their building infrastructure against peers and track building upgrade progress over time. The system can also help other building stakeholders (e.g., building operators, tenants, financiers, and appraisers) understand the relative efficiency of different buildings in a way that is independent from their operations and occupancy. This report outlines the technical protocol used to generate the energy asset score, explains the scoring methodology, and provides additional details regarding the energy asset score tool. This report also describes alternative methods that were considered prior to developing the current approach. Finally, this report describes a few features of the program where alternative approaches are still under evaluation.
building energy use↗
DOE Commercial Building Asset Rating: An Application of Centralized Modeling Tools
This paper presents a novel approach used to develop the U.S. Department of Energy (DOE) Commercial Building Asset Rating, which is intended to help building owners better understand the installed system performance and the total energy use. A simplified data collection and energy-modeling method is employed to disaggregate building energy information. Furthermore, the approach outlined will also include a mechanism for identifying energy improvement opportunities. A detailed modeling approach to formulate an Asset Rating would most likely provide the greatest flexibility and accuracy. Such an approach would, however, require a substantial amount of user investment for collecting the energy audit, data and hiring a professional to perform energy modeling and analysis. A simplified model approach requires fewer input combinations, which could reduce opportunities for error and allow an inexperienced user to quickly develop energy models. However, the accuracy of the results is often questionable. To address the above issue, the method presented in this paper separates model inputs into categories based on overall energy impact, difficulty to obtain, and variability among buildings. We outline an approach that will allow great flexibility in terms of how many and which of the different categories of variables must be found to produce an accurate energy model. The approach will allow all key variables to be inferred from some minimum set of variables while at the same time allowing a user to enter many more variables if he or she has reliable values for them. The approach outlined will also provide constant values for some variables and algorithms for finding those which are very difficult to determine in the field. The whole of this approach will reduce modeling time and expertise required while maintaining accuracy and the ability to support the variability and complexity that exist in buildings. Therefore, the goals of facilitating cost-effective investment in energy efficiency and reducing energy use in the commercial building sector are met.
Asset Rating↗
Renewable Energy Opportunities at Fort Polk, Louisiana
This document provides an overview of renewable resource potential at Fort Polk, based primarily upon analysis of secondary data sources supplemented with limited on-site evaluations. This effort focuses on grid-connected generation of electricity from renewable energy sources and also on ground source heat pumps for heating and cooling buildings. The effort was funded by the U.S. Army Installation Management Command (IMCOM) as follow-on to the 2005 Department of Defense (DoD) Renewables Assessment. The site visit to Fort Polk took place on February 16, 2010.
09 BIOMASS FUELS↗
Aggregate Building Simulator (ABS) Methodology Development, Application, and User Manual
As the relationship between the national building stock and various global energy issues becomes a greater concern, it has been deemed necessary to develop a system of predicting the energy consumption of large groups of buildings. Ideally this system is to take advantage of the most advanced energy simulation software available, be able to execute runs quickly, and provide concise and useful results at a level of detail that meets the users needs without inundating them with data. The resulting methodology that was developed allows the user to quickly develop and execute energy simulations of many buildings simultaneously, taking advantage of parallel processing to greatly reduce total simulation times. The result of these simulations can then be rapidly condensed and presented in a useful and intuitive manner.
99 GENERAL AND MISCELLANEOUS↗