Advantages and disadvantages of different energy consumption benchmarking methods

Advantages and disadvantages of different energy consumption benchmarking methods

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Establishing a scientific and rational benchmarking method for building energy consumption is the key to objectively, justly and accurately evaluating the building energy performance. This paper compares and analyzes the energy consumption benchmarking methods at home and aboard, and proposes a method for determining energy consumption benchmarks b...

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... Ministry of Housing and Urban-Rural Development promulgated the "Standard for energy consumption of building GB/T51161" in 2016; the local construction departments in Beijing, Shanghai, Shenzhen, and Guangdong have also promulgated local building energy consumption standards or guidelines. The comparison of different building energy consumption benchmarking methods at abroad and home see table 1, the advantages and disadvantages of them see Table 2. Establish a scoring model based on the cumulative probability distribution of r values Score≥75, energy performance is high; 50≤ Score < 75, energy performance is general; Score <50, energy performance is low ...
Context 2
... Table 2, we can see that the" Statistical characteristics method based on building sub-classification " is the most simply method, but it is unreasonable and inaccurate, the "Probability method based on building subclassification and the revision of actual EUI" method is more accurate than it, but the revision of actual EUI has not consider all the major factors, which making the comparison of similar buildings not under identical conditions. The above two methods have taken into account the building type and function, but the operating conditions of different buildings are quite different, it is very difficult to establish a building classification model including all the operating conditions. ...

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... These entities account for about a quarter of European buildings' energy consumption [13]. Public buildings (PBs), such as offices, hotels, retail outlets, hospitals, and schools, have a high energy consumption intensity [14]. ...
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... The developed model was then compared with data from apartments in two cities in South Korea. S. Dongmei [10] created and tested a multiple regression model of energy consumption benchmarks based on 45 government buildings in Shenzhen, China. The model was applied to estimate energy consumption benchmarks and evaluate energy performance of two government office buildings in Shenzhen. ...
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... Compared to other studies that addressed machine learning methods for benchmarking buildings, Silva et al. [66] achieved an error rate equal to 18.3% (C-RMSE) using support vector machines for benchmarking school buildings in São Paulo, Brazil. Targeting the same purpose but for office buildings, Dongmei et al. [67] achieved accuracies from 85% to 71% using a regression model. Although the discretisation process implies in loss of information compared to other regressive methods found in the literature, it is possible to see that the approach proposed herein indeed showed good performance compared to such methods. ...
Article
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... Two categories have been recognized as influencing building's energy consumption namely; external factors, which are uncontrollable, yet have significant impacts on the energy consumed through heating and air conditioning system (Dongme 2018). The other category is the internal factor, referring to the factors that maintain the normal function of the building such as the building area, air-conditioned area, building operating time, and the number of staff members. ...
Chapter
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... Two categories have been recognized as influencing building's energy consumption namely; external factors, which are uncontrollable, yet have significant impacts on the energy consumed through heating and air conditioning system (Dongme 2018). The other category is the internal factor, referring to the factors that maintain the normal function of the building such as the building area, air-conditioned area, building operating time, and the number of staff members. ...
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... Two categories have been recognized as influencing building's energy consumption namely; external factors, which are uncontrollable, yet have significant impacts on the energy consumed through heating and air conditioning system (Dongme 2018). The other category is the internal factor, referring to the factors that maintain the normal function of the building such as the building area, air-conditioned area, building operating time, and the number of staff members. ...
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