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| 1 |
ID:
176657
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| Summary/Abstract |
Buildings are by far the largest source of urban energy consumption. In an effort to reduce energy use, cities are mandating that buildings undergo energy benchmarking—the process of measuring building energy performance in order to identify buildings that are inefficient. In this paper, we examine the feasibility of using city-specific, public open data sources in two benchmarking models and compare the results to the same models when using the Commercial Building Energy Consumption Survey (CBECS) dataset, the basis for Energy Star. The two benchmarking models use datasets containing building characteristics and annual energy use from ten major cities. To examine the difference in performance between linear and non-linear models, we use random forest and lasso regression. Results demonstrate that benchmarking models using open data outperform models based solely on the CBECS dataset. Additionally, our results indicate that building area, property type, conditioned area, and water usage are the most important variables for cities to collect. Having demonstrated the benefits of using open data, we recommend two changes to current benchmarking practices: (1) new guidelines that support a data-driven benchmarking framework relying on open data and a transparent modeling process and (2) supporting policies that publicize benchmarking results and incentivize energy savings.
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| 2 |
ID:
126832
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| Publication |
2014.
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| Summary/Abstract |
Many different governments have begun to require disclosure of building energy performance, in order to allow owners and prospective buyers to incorporate this information into their investment decisions. These policies, known as disclosure or information policies, require owners to benchmark their buildings and sometimes conduct engineering audits. However, given substantial variation in the cost to disclose different types of information, it is natural to ask: how much and what kind of information about building energy performance should be disclosed, and for what purposes? To answer this question, this paper assembles and cleans a comprehensive panel dataset of New York City multifamily buildings, and analyzes its predictive power using a Bayesian multilevel regression model. Analysis of variance (ANOVA) reveals that building-level variation is the most important factor in explaining building energy use, and that there are few, if any, relationships of building systems to observed energy use. This indicates that disclosure laws requiring benchmarking data may be relatively more useful than engineering audits in explaining the observed energy performance of existing buildings. These results should inform the further development of information disclosure laws.
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| 3 |
ID:
169869
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| Summary/Abstract |
Climatic zoning has a direct impact on building energy efficiency policies. Currently, most countries adopt simplified weather parameters to define their climatic zoning, with the degree-day method being the most widely used. This widespread use of degree-days has been substantially influenced by the adoption of this indicator by ASHRAE on its climatic zoning, which is a core element for the prescription of requirements for buildings based on their location. However, there is no scientific evidence regarding the agreement between building energy performance and the ASHRAE climatic zones. The objective here was to quantify the mismatch between buildings’ energy performance in each given location and the expected energy performance in the climatic zone they are placed. The study uses a performance-based assessment method relying on building energy simulation and GIS. Climatic zoning performance indicators were calculated based on the energy demand of 52 archetype buildings of the U.S. building stock complying with the ASHRAE Standard 90.1–2013. Results suggest that the stipulated climatic zone misclassifies 10% of the area evaluated, potentially misclassifying highly populated urban areas. These misclassifications have direct impact on the building energy efficiency policies of a given location, which may not be the most adequate for its climate.
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