Sampling for building energy consumption with fuzzy theory
| Autor: | Jinghong Qin，Jili Zhang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Consumption (economics)
Mathematical optimization Fuzzy clustering 020209 energy Mechanical Engineering 0211 other engineering and technologies Sampling (statistics) 02 engineering and technology Building and Construction computer.software_genre Fuzzy logic Data set Sample size determination 021105 building & construction 0202 electrical engineering electronic engineering information engineering Data mining Electrical and Electronic Engineering Cluster analysis computer Energy (signal processing) Civil and Structural Engineering Mathematics |
| Zdroj: | Energy and Buildings. 156:78-84 |
| ISSN: | 0378-7788 |
| DOI: | 10.1016/j.enbuild.2017.09.047 |
| Popis: | The foundation of energy saving is knowing the real status of building energy consumption. For various kinds and a great number of building energy consumption data, the fuzzy theory is applied for sampling. It would make data representational. Firstly, a fuzzy clustering method is used to classify the data set and then the samples are extracted from the subclass. A modified clustering algorithm based on entropy weight method is proposed. It can determine the number of the classification of data set. The simulation results indicate that the new method can directly determine the optimal sample size. This method is suitably applied for dynamic energy consumption data and is more accurate compared with the statistical method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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