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Regional Energy Simulation Methods: Identifying, Evaluating, and Comparing Methods to Support the Generation of Virtual Building Stocks at the Sub-national Level.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Regional Energy Simulation Methods: Identifying, Evaluating, and Comparing Methods to Support the Generation of Virtual Building Stocks at the Sub-national Level./
作者:	Hendricken, Liam.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	163 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-06, Section: B.
Contained By:	Dissertations Abstracts International80-06B.
標題:	Architectural engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10975652
ISBN:	9780438710641

Regional Energy Simulation Methods: Identifying, Evaluating, and Comparing Methods to Support the Generation of Virtual Building Stocks at the Sub-national Level.
Hendricken, Liam.

Regional Energy Simulation Methods: Identifying, Evaluating, and Comparing Methods to Support the Generation of Virtual Building Stocks at the Sub-national Level. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 163 p.

Source: Dissertations Abstracts International, Volume: 80-06, Section: B.

Thesis (Ph.D.)--Drexel University, 2018.

This item must not be sold to any third party vendors.

In the United States, buildings account for approximately 40% of primary energy use and 75% of electrical energy use on an annual basis (U.S. Department of Energy 2017), making them an important target for energy reduction. However, it is believed that the rate at which building energy is being reduced could be improved. The energy efficiency gap is a hypothetical phenomenon which identifies that the reduction of building energy consumption may be occurring more slowly than possible due to a number of market barriers (Jaffe and Stavins 1994). While a number of top-down or disaggregative approaches and tools, such as Scout (Harris, et al. 2016), have been developed in an attempt to promote energy conservation measure (ECM) adoption in buildings and reduce the energy efficiency gap, it is posed that bottom-up approaches may enable more rapid change (Koopmans and Willem te Velde 2001). However, bottom-up approaches rely heavily upon the use of virtual building stocks: databases or information sets representing a set of actual buildings or variations of actual buildings in order to investigate topics such as energy consumption, waste production, or building energy demand (Kohler and Hassler 2002). Unfortunately, virtual building stocks are difficult to develop due to a lack of openly-available, comprehensive data. Furthermore, virtual building stocks are considered computationally costly and time consuming to execute since building performance simulation (BPS) is required to estimate the complex interactions associated with ECM implementation and the heat replacement effect (Wittchen and Aggerholm 2000) on these virtual building stocks. To overcome the lack of characterization data and computational cost, a number of shortcuts have been developed: the use of representative energy models (REMs), reduced order methods (RMs), and heuristic ECM search methods. As opposed to a single building energy model (SBEM) which is a single model representing a single building, a representative energy model (REM) is a single model representing two or more buildings, reducing relative computational costs but potentially incorporating prediction error. RMs such as log-addition (Surana, et al. 2012) are more rapid surrogates for BPS, but they have not been proven to reliably and accurately predict energy savings relative to BPS. While heuristic ECM search methods can be intelligent and efficient replacements for exhaustive enumeration, ensuring they identify global optimal solution sets can be challenging (De Jong 2007). It is unclear from literature how these shortcuts contribute to the market barrier of imperfect information, and what level of uncertainty they generate when used in bottomup approaches. This dissertation reviewed existing surrogates for BPS and identified a new reduced order method for predicting the energy savings of combinations of ECMs. Termed the log-additive decomposition (LAD) method, it was benchmarked against existing reduced order methods proposed in literature in a testbed representing medium offices in the Greater Philadelphia Region. The testbed utilized two REMs based on CoStar data (CoStar Realty 2012), seventy ECMs, and was built around jEPlus (Y. Zhang 2012). Conducting an exhaustive enumeration via BPS for all possible ECM combinations, which consists of 57,286,656 possible combinations, would require multiple years to simulate. In place of conducting an exhaustive enumeration, LAD was compared to BPS for all possible two-way ECM interactions and a more manageable 30,000, randomly selected ECM combinations. Results indicated that LAD requires 0.018% of the computational cost of a typical BPS (EnergyPlus) but has average prediction error in the range of 10%, a large improvement over other methods which have average errors in the range of 20%-50%. Utilizing the same testbed to refine and hone the LAD method, LAD was used in a hybrid ECM search method and compared to an ad-hoc ECM search method (commonly used in practice), based on professional judgment (Wen, et al. 2013), and a heuristic ECM search method (commonly used in research), using jEPlus+EA (Y. Zhang 2018). Since exhaustive enumeration was not possible, the ECM search methods were compared in a permutation space where the global optimal solution was unknown. Using both identified performance assessment metrics (Knowles, Thiele and Zitzler 2006) and new metrics, results indicated that the ad-hoc ECM search method results in highly sub-optimal results. Alternately, the LAD and heuristic ECM search methods performed well with similar results. Overall, a hybrid ECM search method using LAD performed well, providing consistent results, and overcoming the knowledge required with applying and tuning optimization parameters (De Jong 2007). Following refinement and verification of the LAD method, this method was implemented in a new testbed to quantify the impact of using REMs, instead of SBEMs, on projected regional retrofit costs, projected regional energy savings, and differences in ECMs identified as cost-effective for the geography under evaluation. (Abstract shortened by ProQuest.).

ISBN: 9780438710641Subjects--Topical Terms:

3174102
Architectural engineering.

Regional Energy Simulation Methods: Identifying, Evaluating, and Comparing Methods to Support the Generation of Virtual Building Stocks at the Sub-national Level.
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520 $a In the United States, buildings account for approximately 40% of primary energy use and 75% of electrical energy use on an annual basis (U.S. Department of Energy 2017), making them an important target for energy reduction. However, it is believed that the rate at which building energy is being reduced could be improved. The energy efficiency gap is a hypothetical phenomenon which identifies that the reduction of building energy consumption may be occurring more slowly than possible due to a number of market barriers (Jaffe and Stavins 1994). While a number of top-down or disaggregative approaches and tools, such as Scout (Harris, et al. 2016), have been developed in an attempt to promote energy conservation measure (ECM) adoption in buildings and reduce the energy efficiency gap, it is posed that bottom-up approaches may enable more rapid change (Koopmans and Willem te Velde 2001). However, bottom-up approaches rely heavily upon the use of virtual building stocks: databases or information sets representing a set of actual buildings or variations of actual buildings in order to investigate topics such as energy consumption, waste production, or building energy demand (Kohler and Hassler 2002). Unfortunately, virtual building stocks are difficult to develop due to a lack of openly-available, comprehensive data. Furthermore, virtual building stocks are considered computationally costly and time consuming to execute since building performance simulation (BPS) is required to estimate the complex interactions associated with ECM implementation and the heat replacement effect (Wittchen and Aggerholm 2000) on these virtual building stocks. To overcome the lack of characterization data and computational cost, a number of shortcuts have been developed: the use of representative energy models (REMs), reduced order methods (RMs), and heuristic ECM search methods. As opposed to a single building energy model (SBEM) which is a single model representing a single building, a representative energy model (REM) is a single model representing two or more buildings, reducing relative computational costs but potentially incorporating prediction error. RMs such as log-addition (Surana, et al. 2012) are more rapid surrogates for BPS, but they have not been proven to reliably and accurately predict energy savings relative to BPS. While heuristic ECM search methods can be intelligent and efficient replacements for exhaustive enumeration, ensuring they identify global optimal solution sets can be challenging (De Jong 2007). It is unclear from literature how these shortcuts contribute to the market barrier of imperfect information, and what level of uncertainty they generate when used in bottomup approaches. This dissertation reviewed existing surrogates for BPS and identified a new reduced order method for predicting the energy savings of combinations of ECMs. Termed the log-additive decomposition (LAD) method, it was benchmarked against existing reduced order methods proposed in literature in a testbed representing medium offices in the Greater Philadelphia Region. The testbed utilized two REMs based on CoStar data (CoStar Realty 2012), seventy ECMs, and was built around jEPlus (Y. Zhang 2012). Conducting an exhaustive enumeration via BPS for all possible ECM combinations, which consists of 57,286,656 possible combinations, would require multiple years to simulate. In place of conducting an exhaustive enumeration, LAD was compared to BPS for all possible two-way ECM interactions and a more manageable 30,000, randomly selected ECM combinations. Results indicated that LAD requires 0.018% of the computational cost of a typical BPS (EnergyPlus) but has average prediction error in the range of 10%, a large improvement over other methods which have average errors in the range of 20%-50%. Utilizing the same testbed to refine and hone the LAD method, LAD was used in a hybrid ECM search method and compared to an ad-hoc ECM search method (commonly used in practice), based on professional judgment (Wen, et al. 2013), and a heuristic ECM search method (commonly used in research), using jEPlus+EA (Y. Zhang 2018). Since exhaustive enumeration was not possible, the ECM search methods were compared in a permutation space where the global optimal solution was unknown. Using both identified performance assessment metrics (Knowles, Thiele and Zitzler 2006) and new metrics, results indicated that the ad-hoc ECM search method results in highly sub-optimal results. Alternately, the LAD and heuristic ECM search methods performed well with similar results. Overall, a hybrid ECM search method using LAD performed well, providing consistent results, and overcoming the knowledge required with applying and tuning optimization parameters (De Jong 2007). Following refinement and verification of the LAD method, this method was implemented in a new testbed to quantify the impact of using REMs, instead of SBEMs, on projected regional retrofit costs, projected regional energy savings, and differences in ECMs identified as cost-effective for the geography under evaluation. (Abstract shortened by ProQuest.).
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