The Experts below are selected from a list of 201 Experts worldwide ranked by ideXlab platform
Travis Walter - One of the best experts on this subject based on the ideXlab platform.
-
a regression based approach to estimating retrofit savings using the building Performance Database
Applied Energy, 2016Co-Authors: Travis Walter, Michael D SohnAbstract:Abstract Retrofitting building systems is known to provide cost-effective energy savings. However, prioritizing retrofits and computing their expected energy savings and cost/benefits can be a complicated, costly, and an uncertain effort. Prioritizing retrofits for a portfolio of buildings can be even more difficult if the owner must determine different investment strategies for each of the buildings. Meanwhile, we are seeing greater availability of data on building energy use, characteristics, and equipment. These data provide opportunities for the development of algorithms that link building characteristics and retrofits empirically. In this paper we explore the potential of using such data for predicting the expected energy savings from equipment retrofits for a large number of buildings. We show that building data with statistical algorithms can provide savings estimates when detailed energy audits and physics-based simulations are not cost- or time-feasible. We develop a multivariate linear regression model with numerical predictors (e.g., operating hours, occupant density) and categorical indicator variables (e.g., climate zone, heating system type) to predict energy use intensity. The model quantifies the contribution of building characteristics and systems to energy use, and we use it to infer the expected savings when modifying particular equipment. We verify the model using residual analysis and cross-validation. We demonstrate the retrofit analysis by providing a probabilistic estimate of energy savings for several hypothetical building retrofits. We discuss the ways understanding the risk associated with retrofit investments can inform decision making. The contributions of this work are the development of a statistical model for estimating energy savings, its application to a large empirical building dataset, and a discussion of its use in informing building retrofit decisions.
-
big data for building energy Performance lessons from assembling a very large national Database of building energy use
Applied Energy, 2015Co-Authors: Paul Mathew, Michael D Sohn, Laurel N Dunn, Andrea Mercado, Claudine Custudio, Travis WalterAbstract:Abstract Building energy data has been used for decades to understand energy flows in buildings and plan for future energy demand. Recent market, technology and policy drivers have resulted in widespread data collection by stakeholders across the buildings industry. Consolidation of independently collected and maintained datasets presents a cost-effective opportunity to build a Database of unprecedented size. Applications of the data include peer group analysis to evaluate building Performance, and data-driven algorithms that use empirical data to estimate energy savings associated with building retrofits. This paper discusses technical considerations in compiling such a Database using the DOE Buildings Performance Database (BPD) as a case study. We gathered data on over 750,000 residential and commercial buildings. We describe the process and challenges of mapping and cleansing data from disparate sources. We analyze the distributions of buildings in the BPD relative to the Commercial Building Energy Consumption Survey (CBECS) and Residential Energy Consumption Survey (RECS), evaluating peer groups of buildings that are well or poorly represented, and discussing how differences in the distributions of the three datasets impact use-cases of the data. Finally, we discuss the usefulness and limitations of the current dataset and the outlook for increasing its size and applications.
Boanerges Alemanmeza - One of the best experts on this subject based on the ideXlab platform.
-
designing a high Performance Database engine for the db4xml native xml Database system
Journal of Systems and Software, 2004Co-Authors: Sudhanshu Sipani, Kunal Verma, John A Miller, Boanerges AlemanmezaAbstract:eXtensible Markup Language (XML) is fast becoming the common electronic data interchange language between applications. In this paper, we describe a Database engine called 'Db4XML', which provides storage for XML documents in native format. Db4XML is a high Performance, main memory resident Database engine. Db4XML is being used as a testbed for comparing various query evaluation techniques. The use of wild card (*, ?, '//', etc.) in the path expressions of a query allows users to query documents whose structural information is not available. This paper lists different techniques that can be used to evaluate generalized path expressions (GPE) and presents a Performance comparison of the same. A preliminary Performance study of the effect of using concurrency control techniques on the various query evaluation techniques is also performed. This paper briefly discusses a suitable recovery technique for the Database engine.
Michael D Sohn - One of the best experts on this subject based on the ideXlab platform.
-
a regression based approach to estimating retrofit savings using the building Performance Database
Applied Energy, 2016Co-Authors: Travis Walter, Michael D SohnAbstract:Abstract Retrofitting building systems is known to provide cost-effective energy savings. However, prioritizing retrofits and computing their expected energy savings and cost/benefits can be a complicated, costly, and an uncertain effort. Prioritizing retrofits for a portfolio of buildings can be even more difficult if the owner must determine different investment strategies for each of the buildings. Meanwhile, we are seeing greater availability of data on building energy use, characteristics, and equipment. These data provide opportunities for the development of algorithms that link building characteristics and retrofits empirically. In this paper we explore the potential of using such data for predicting the expected energy savings from equipment retrofits for a large number of buildings. We show that building data with statistical algorithms can provide savings estimates when detailed energy audits and physics-based simulations are not cost- or time-feasible. We develop a multivariate linear regression model with numerical predictors (e.g., operating hours, occupant density) and categorical indicator variables (e.g., climate zone, heating system type) to predict energy use intensity. The model quantifies the contribution of building characteristics and systems to energy use, and we use it to infer the expected savings when modifying particular equipment. We verify the model using residual analysis and cross-validation. We demonstrate the retrofit analysis by providing a probabilistic estimate of energy savings for several hypothetical building retrofits. We discuss the ways understanding the risk associated with retrofit investments can inform decision making. The contributions of this work are the development of a statistical model for estimating energy savings, its application to a large empirical building dataset, and a discussion of its use in informing building retrofit decisions.
-
big data for building energy Performance lessons from assembling a very large national Database of building energy use
Applied Energy, 2015Co-Authors: Paul Mathew, Michael D Sohn, Laurel N Dunn, Andrea Mercado, Claudine Custudio, Travis WalterAbstract:Abstract Building energy data has been used for decades to understand energy flows in buildings and plan for future energy demand. Recent market, technology and policy drivers have resulted in widespread data collection by stakeholders across the buildings industry. Consolidation of independently collected and maintained datasets presents a cost-effective opportunity to build a Database of unprecedented size. Applications of the data include peer group analysis to evaluate building Performance, and data-driven algorithms that use empirical data to estimate energy savings associated with building retrofits. This paper discusses technical considerations in compiling such a Database using the DOE Buildings Performance Database (BPD) as a case study. We gathered data on over 750,000 residential and commercial buildings. We describe the process and challenges of mapping and cleansing data from disparate sources. We analyze the distributions of buildings in the BPD relative to the Commercial Building Energy Consumption Survey (CBECS) and Residential Energy Consumption Survey (RECS), evaluating peer groups of buildings that are well or poorly represented, and discussing how differences in the distributions of the three datasets impact use-cases of the data. Finally, we discuss the usefulness and limitations of the current dataset and the outlook for increasing its size and applications.
Sudhanshu Sipani - One of the best experts on this subject based on the ideXlab platform.
-
designing a high Performance Database engine for the db4xml native xml Database system
Journal of Systems and Software, 2004Co-Authors: Sudhanshu Sipani, Kunal Verma, John A Miller, Boanerges AlemanmezaAbstract:eXtensible Markup Language (XML) is fast becoming the common electronic data interchange language between applications. In this paper, we describe a Database engine called 'Db4XML', which provides storage for XML documents in native format. Db4XML is a high Performance, main memory resident Database engine. Db4XML is being used as a testbed for comparing various query evaluation techniques. The use of wild card (*, ?, '//', etc.) in the path expressions of a query allows users to query documents whose structural information is not available. This paper lists different techniques that can be used to evaluate generalized path expressions (GPE) and presents a Performance comparison of the same. A preliminary Performance study of the effect of using concurrency control techniques on the various query evaluation techniques is also performed. This paper briefly discusses a suitable recovery technique for the Database engine.
Omar Smadi - One of the best experts on this subject based on the ideXlab platform.
-
use of pavement management information system for verification of mechanistic empirical pavement design guide Performance predictions
Transportation Research Record, 2010Co-Authors: Sunghwan Kim, Halil Ceylan, Kasthurirangan Gopalakrishnan, Omar SmadiAbstract:The Performance models used in the Mechanistic-Empirical Pavement Design Guide (MEPDG) are nationally calibrated with design inputs and Performance data obtained primarily from the national Long-Term Pavement Performance Database. It is necessary to verify and calibrate MEPDG Performance models for local highway agencies' implementation by taking into account local materials, traffic information, and environmental conditions. This paper discusses the existing pavement management information system (PMIS) with respect to the MEPDG and the accuracy of the nationally calibrated MEPDG prediction models for Iowa highway conditions. All the available PMIS data for Interstate and primary road systems in Iowa were retrieved from the Iowa Department of Transportation (DOT) PMIS. The retrieved Databases were then compared and evaluated with respect to the input requirements and outputs for Version 1.0 of the MEPDG software. Using Iowa DOT's comprehensive PMIS Database, researchers selected 16 types of pavement sect...
