### Abstract

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.

Original language | English (US) |
---|---|

Pages (from-to) | 372-379 |

Number of pages | 8 |

Journal | Advances in Science, Technology and Engineering Systems |

Volume | 3 |

Issue number | 6 |

State | Published - Jan 1 2018 |

### Fingerprint

### Keywords

- Data Mining
- Energy Consumption
- Partial-Value Association
- Structural System Model

### ASJC Scopus subject areas

- Engineering (miscellaneous)
- Management of Technology and Innovation
- Physics and Astronomy (miscellaneous)

### Cite this

*Advances in Science, Technology and Engineering Systems*,

*3*(6), 372-379.

**Modeling an energy consumption system with partial-value data associations.** / Ye, Nong; Fok, Ting Yan; Chong, Oswald.

Research output: Contribution to journal › Article

*Advances in Science, Technology and Engineering Systems*, vol. 3, no. 6, pp. 372-379.

}

TY - JOUR

T1 - Modeling an energy consumption system with partial-value data associations

AU - Ye, Nong

AU - Fok, Ting Yan

AU - Chong, Oswald

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Data Mining

KW - Energy Consumption

KW - Partial-Value Association

KW - Structural System Model

UR - http://www.scopus.com/inward/record.url?scp=85061769831&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061769831&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85061769831

VL - 3

SP - 372

EP - 379

JO - Advances in Science, Technology and Engineering Systems

JF - Advances in Science, Technology and Engineering Systems

SN - 2415-6698

IS - 6

ER -