# Adjustment of Multiple Variables for Optimal Control of Building Energy Performance via a Genetic Algorithm

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## Abstract

**:**

## 1. Introduction

## 2. Building Model and HVAC System Description

#### 2.1. Data Generation

#### 2.2. HVAC Systems and Control Set Points

## 3. Genetic Algorithm-Based Optimal Control Variable Calculations and Optimal Control Scenario Composition

#### 3.1. Determination of Genetic Algorithm-Based Optimal Control Variables

#### 3.2. Genetic Algorithm Calculation and Control Range Settings

#### 3.3. Optimal Control Scenarios Composition and Evaluation Method

## 4. Results and Discussion

#### 4.1. Variance of Control Parameters

#### 4.1.1. Supply Air Temperature

#### 4.1.2. Duct Static Pressure

#### 4.1.3. Chilled Water Temperature

#### 4.1.4. Pump Pressure Difference

#### 4.2. Analysis of Energy Consumption

#### 4.2.1. Monthly Energy Consumption Comparison

#### 4.2.2. Total Energy Consumption Comparison

#### 4.2.3. Energy Consumption Comparison by Component

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 8.**Monthly energy consumption comparison between normal operation mode and optimal operation modes for June through September.

**Figure 9.**Comparison of total energy consumption between normal operation mode and optimal operation modes for June through September.

**Figure 10.**Comparison of fan energy consumption between normal operation mode and optimal operation modes for June through September.

**Figure 11.**Comparison of chiller energy consumption between normal operation mode and optimal operation modes for June through September.

**Figure 12.**Comparison of pump energy consumption between normal operation mode and optimal operation modes for June through September.

**Table 1.**Simulation conditions of the reference building: Large-scale office building [12].

Component | Features |
---|---|

Weather Data and Site Location | TRY Seoul Latitude: 37.57° N, longitude: 126.97° E |

Building Type | Large-Scale Office |

Total Building Area (m^{2}) | 46,320 |

Hours Simulated (h) | 2928 |

Envelope U-Factor (m^{2} K/W) | External Wall 0.35 Roof 0.213 External Window 1.5 |

Window-Wall Ratio (%) | 40 |

Set Point (°C) | Cooling 26 Heating 20 |

Internal Gain | Lighting 10.76 (W/m^{2})People 18.58 (m ^{2}/person)Plug and Process 10.76 (W/m ^{2}) |

People Activity Level | 1.15 METs |

HVAC Sizing | Auto Calculated (Determine Simulation Program) |

Building and HVAC Operation Schedule | 7:00–18:00 |

Output Time-Step | Hourly (1 h) |

Supply Air Temperature (°C) | Duct Static Pressure (Pa) | Chilled Water Temperature (°C) | Pump Pressure Difference (kPa) |
---|---|---|---|

12–19 | 250–622 | 6–10 | 34.47–103.43 |

Case | Control Variable | |||
---|---|---|---|---|

Supply Air Temperature (°C) | Duct Static Pressure (Pa) | Chilled Water Supply Temperature (°C) | Pump Pressure Difference (kPa) | |

(Non-Optimal) | 12.8 | 474 | 6.7 | 78.05 |

Case 1 | Calculate GA | Calculate GA | 6.7 | 78.05 |

Case 2 | 12.8 | 474 | Calculate GA | Calculate GA |

Case 3 | Calculate GA | Calculate GA | Calculate GA | Calculate GA |

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**MDPI and ACS Style**

Seong, N.-C.; Kim, J.-H.; Choi, W.
Adjustment of Multiple Variables for Optimal Control of Building Energy Performance via a Genetic Algorithm. *Buildings* **2020**, *10*, 195.
https://doi.org/10.3390/buildings10110195

**AMA Style**

Seong N-C, Kim J-H, Choi W.
Adjustment of Multiple Variables for Optimal Control of Building Energy Performance via a Genetic Algorithm. *Buildings*. 2020; 10(11):195.
https://doi.org/10.3390/buildings10110195

**Chicago/Turabian Style**

Seong, Nam-Chul, Jee-Heon Kim, and Wonchang Choi.
2020. "Adjustment of Multiple Variables for Optimal Control of Building Energy Performance via a Genetic Algorithm" *Buildings* 10, no. 11: 195.
https://doi.org/10.3390/buildings10110195