# An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room

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## Abstract

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## 1. Introduction

## 2. Methodology

#### 2.1. Development of a GEP Model

- Selecting the terminal set, which consists of the same variables as the problem’s independent variables and the system’s state variables. This step involves the selection of the fit function, which typically employs the root-mean-square error (RMSE);
- Selecting a collection of functions, including mathematical operators, test functions, and Boolean functions;
- The model accuracy measurement index is used to determine the extent to which the model is capable of solving a specific problem;
- Control components, including the numerical component values and qualitative variables, are used to control the program’s execution;
- The number of data in the training section, the number of data in the test sections of the chromosomes, the size of the head, the number of genes, and the choice of the transplant operator, which can be adjusted with four options of addition, subtraction, multiplication, and division.

_{act}is the actual cost of energy, CoE(i)

_{for}is the forecasted cost of energy using GEP or the optimized model, and m is the total number of data.

#### 2.2. Optimization Method

_{s}) and the types of Candida (N)-generating units.

## 3. Results and Discussion

#### 3.1. Sensitivity Analysis

#### 3.2. The Results of Model

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

## References

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**Figure 6.**Comparative diagram of the validation results of the best GEP obtained for a number of different models.

**Figure 7.**Comparative diagram of the validation result of the best GEP trained in predicting energy consumption during operations per unit area (million rails per square meter).

Annual Values | Mumbai, |
---|---|

India | |

Daytime maximum temperature | 31.50 °C |

Daily low temperature | 18.30 °C |

Humidity | 53% |

Precipitation | 653 mm |

Rain days | 44.4 days |

Hours of sunshine | 2592 h |

Genetic Functions | General Settings | ||
---|---|---|---|

Mutation rate | 0.046 | Chromosome number | 32 |

Inversion rate | 0.2 | Vertex size | 8 |

Insertion frequency | 0.15 | Number of genes per chromosome | 4 |

Insertion rate | 0.1 | The number of productive populations | 1150 |

Compounding single point | 0.37 |

Model ID | Input Variables |
---|---|

Design Combo 1 (D1) | D_{t}, T_{t−1}, WWR_{t} |

Design Combo 2 (D2) | D_{t}, T_{t−3}, H_{t−1}, WWR_{t} |

Design Combo 3 (D3) | D_{t}, T_{t−1}, H_{t−1}, WWR_{t} |

Design Combo 4 (D4) | D_{t}, T_{t−2}, T_{t−3}, H_{t−2}, H_{t−1,} WWR_{t} |

Model ID | MAE | RMSE | R | MAE | RMSE | R |
---|---|---|---|---|---|---|

Training | Testing | |||||

D1 | 0.01818 | 0.14567 | 0.88776 | 0.01920 | 0.14567 | 0.86528 |

D2 | 0.01631 | 0.089646 | 0.92956 | 0.01812 | 0.09146 | 0.90825 |

D3 | 0.01762 | 0.09220 | 0.90319 | 0.01895 | 0.10220 | 0.88526 |

D4 | 0.01925 | 0.12134 | 0.91351 | 0.01735 | 0.12134 | 0.89543 |

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**MDPI and ACS Style**

Liu, K.-S.; Muda, I.; Lin, M.-H.; Dwijendra, N.K.A.; Carrillo Caballero, G.; Alviz-Meza, A.; Cárdenas-Escrocia, Y.
An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room. *Sustainability* **2023**, *15*, 1728.
https://doi.org/10.3390/su15021728

**AMA Style**

Liu K-S, Muda I, Lin M-H, Dwijendra NKA, Carrillo Caballero G, Alviz-Meza A, Cárdenas-Escrocia Y.
An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room. *Sustainability*. 2023; 15(2):1728.
https://doi.org/10.3390/su15021728

**Chicago/Turabian Style**

Liu, Kuang-Sheng, Iskandar Muda, Ming-Hung Lin, Ngakan Ketut Acwin Dwijendra, Gaylord Carrillo Caballero, Aníbal Alviz-Meza, and Yulineth Cárdenas-Escrocia.
2023. "An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room" *Sustainability* 15, no. 2: 1728.
https://doi.org/10.3390/su15021728