Modeling an Energy Consumption System with Partial-Value Data Associations
| Autor: | Nong Ye, Ting Yan Fok, Oswald Chong |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Physics and Astronomy (miscellaneous)
lcsh:T 020209 energy 02 engineering and technology Energy consumption 010501 environmental sciences lcsh:Technology 01 natural sciences Management of Technology and Innovation Statistics 0202 electrical engineering electronic engineering information engineering lcsh:Q lcsh:Science Engineering (miscellaneous) Value (mathematics) 0105 earth and related environmental sciences Mathematics |
| Zdroj: | Advances in Science, Technology and Engineering Systems, Vol 3, Iss 6 (2018) |
| ISSN: | 2415-6698 |
| Popis: | Many existing system modeling techniques based on statistical modeling, data mining and machine learning have a shortcoming of building variable relations for the full ranges of variable values using one model, although certain variable relations may hold for only some but not all variable values. This shortcoming is overcome by the Partial-Value Association Discovery (PVAD) algorithm that is a new multivariate analysis algorithm to learn both full-value and partial-value relations of system variables from system data. Our research used the PVAD algorithm to model variable relations of energy consumption from data by learning full-and partial-value variable relations of energy consumption. The PVAD algorithm was applied to data of energy consumption obtained from a building at Arizona State University (ASU). Full- and partial-value variable associations of building energy consumption from the PVAD algorithm are compared with variable relations from a decision tree algorithm applied to the same data to show advantages of the PVAD algorithm in modeling the energy consumption system. |
| Databáze: | OpenAIRE |
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