Energy efficiency of commercial and institutional buildings is a topic that continues to gain interest as sustainability, green and high performance buildings attract the attention of building owners, facility managers, engineers, architects and other professionals within the built environment. To support this area of interest, tools are needed. The aim of this research is to develop and test the A Framework to Improving Building Operations Decisions for Energy Efficiency. The Framework was developed through an analysis of existing literature, case studies and questionnaire findings, and insight from industry experts. The Framework links energy and maintenance management decisions for heating, ventilating and air-conditioning (HV AC) systems to help facility managers and others within the built environment transition from reactive to pro active practices to support sustainability and energy efficiency goals. Review of existing literature found that energy and maintenance management practices are often researched and applied in practice separately. However, as evidenced by current practices, even if the most energy efficient equipment is installed, without proper maintenance, it will not remain energy efficient. Thus, the Framework seeks to help decision makers consider both energy and maintenance management within the same decision making process. The Framework consists of a Needs Assessment, Framework Architecture, Decision Support System and Implementation Evaluator. The Needs Assessment is a decision tree to quickly evaluate if the Framework will be useful to a potential user. The Framework Architecture provides a visual representation of the independency between energy and maintenance management. The Decision Support System, the main part of the Framework, consists of a set of multiple choice questions, a series of processing algorithms and a Recommendations Report. To use the Decision Support System, a user answers the set of questions. After the researcher uses a set of algorithms to processes the user's responses, the user receives a Recommendations Report. The Recommendations Report contains 1) three recommended actions that the user can evaluate and determine if the actions can be implemented within his/her facility to improve energy and/or maintenance management practices and 2) a Proactive/Reactive IV Score. This quantitative score compares how pro active or reactive the practices at the facility the question set was completed for to perceived best in class practices. The Decision Support System was tested by 56 industry participants and evaluated by 31 of the test participants. The [mal part of the Framework, the Implementation Evaluator, is a process map to help Framework users evaluate and implement the Framework recommendations and move towards continuous improvement within their facility management organization. The results of the Framework testing found the Framework was helpful or very helpful to over 60 percent of evaluators, and that the framework was especially helpful for making combined energy and maintenance management decisions. However, like many testing process involving user evaluations, it is important to acknowledge that the results likely reflect a non-response bias, as well as an optimum bias. Contributions to knowledge resulting from the research include identifying and documenting the interdependency between energy and maintenance management; documentation of 35 energy, maintenance and human factors practices and documenting the importance of goal setting and use of goals to support. effective energy and maintenance management. Some of the practices documented include the development of a maintenance plan, the need to regularly calibrate sensors and meters, the use of metrics for energy and maintenance management decision making, benchmarking energy performance and providing energy and maintenance training. v
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:567592 |
| Date | January 2013 |
| Creators | Lewis, Angela |
| Publisher | University of Reading |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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