# Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System

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## Abstract

**:**

_{2}production, and the ant colony algorithm was used for optimization. According to the findings, the ratio of the north and east windows to the wall in one direction is 70 percent, while the ratio of the south window to the wall in the same direction ranges from 35 to 50 percent. When the ratio and percentage of the west window to the west wall is between 60 and 70 percent, the amount of produced energy and CO

_{2}is reduced to negligible levels.

## 1. Introduction

_{2}, have a significant impact on both climate change and the warming of the planet [5]. Climate change and global warming cause many challenges to the world such as increases in urban floods [6,7,8]. The first step towards achieving smart buildings is energy consumption analysis and optimization. The growth of electric energy consumption and its dependence on polluting fossil fuels, especially in domestic and commercial uses, has led researchers to think of ways to control the amount of energy consumption in buildings [9]. Therefore, it is very important to provide solutions that can reduce energy consumption in this sector [10]. Compliance with the smallest details can have a great impact on reducing energy consumption in buildings; for example, the way the building is oriented, the way the side spaces are located, and the improvement of insulation methods with the lowest cost can improve the energy efficiency of buildings, and thus by correcting the construction methods building design can achieve ideal design [11,12,13].

_{2}is studied. As input data for the soft computing and optimization method, the building’s physical characteristics, such as the window-to-wall ratio, were evaluated. By examining this topic, we attempt to answer the question of how the physical characteristics of a school building affect energy consumption and CO

_{2}emissions.

## 2. Materials and Methods

#### 2.1. Case Study

#### 2.2. DesignBuilder Model Validation

^{2}k. The materials utilized in the construction of the external-to-interior brick wall are exterior brick and plaster and the heat transfer coefficient of the wall in this instance is 2.650 W/m

^{2}k.

^{2}k. The materials utilized in the concrete wall from the exterior to the interior of the building are light concrete and plaster and the heat transfer coefficient of the wall in this instance is 2.291 W/m

^{2}k.

_{2}emitted. The key element in this section is that the brick wall is compared to the concrete wall, the beam ceiling is compared to the concrete slab, triple- and double-glazed glass is compared to regular glass, and wall and ceiling insulation is 40 mm thick in optimization. The building energy is effective and generates reduced energy loss; therefore, it has been added as an effective choice in the DesignBuilder optimization. The window-to-wall percentages on the north, south, east, and west fronts, on the other hand, are defined as 15, 20, 45, and 60 percent, and the output of this component includes the total energy and CO

_{2}.

#### 2.3. Support Vector Machine (SVM)

^{m}y), y is the output value (y ∈ R

^{m}), w is the weight vector (w ∈ R

^{m}), and b is the bias (b ∈ R

^{m})

^{*}are obtained. These coefficients are called Lagrange coefficients.

_{i}is the input vector with which the model is trained, x is the input vector, x

_{r}and x

_{s}are two vectors, w

_{0}supports the optimal weight vector, and b

_{0}is the optimal bias value. Data whose corresponding Lagrange coefficients are non-zero are known as support vectors. From the point of view of geometry, these data have a bigger prediction error than $\pm \epsilon $, so the support vectors are not included in the range of $\pm \epsilon $ and it controls the number of support vectors. According to the relationship, it is observed that the data whose Lagrange coefficient are zero do not play a role in the final answer; in other words, they are support vectors that determine the final regression function with the optimal answer [24].

#### 2.4. Ant Colony System (ACS)

_{0}of its next task according to the Equation (6).

_{0}of its next task according to the Equation (7).

_{ij}is the length of the bow (i, j), ${\eta}_{ij}=\frac{1}{{d}_{i{j}_{}}}$ represents the influence of this innovative value, and β is the innovative value. N

^{k}is the set of candidate actions of the ant for its next move.

^{k}. In other words, until the end of the solution-creation process, ant k no longer has the right to choose the work. It should also be said that at the beginning of the solution-creation process, N

^{k}= V 1 ≤ k ≤ K, where V is the set of all tasks of the problem.

^{bs}, is updated according to Equation (9).

^{bs}. Of course, these actions encourage other ants to repeat this tour.

## 3. Results and Discussion

_{2}makes up the output of the model. In other words, this research involved the training of two different networks: one network was trained to collect energy, while the second network was trained to obtain CO

_{2}. Because training data and test data are both utilized in the process of training the network, the results that the network produces for these two distinct types of data have been analyzed.

_{2}produced by trains. The precision of the network’s training is demonstrated by the fact that the amount of energy that is computed by the network is very near to its actual value for each of the data points. Figure 5b and Figure 6b, meanwhile, illustrate the statistics on the use of energy and the production of CO

_{2}, respectively, for the tests.

## 4. Results of Optimization

_{2}. Therefore, ACS are executed once for energy consumption and once for CO

_{2}, and the results are analyzed. The implementation of the algorithm shows that for both objective functions, the input values are almost the same.

_{2}production are given. The optimal energy consumption is equal to 3.512 × 10

^{4}kWh and the amount of CO

_{2}production is equal to 2.151 × 10

^{4}kg (Figure 7).

_{2}dioxide that is produced is reduced to a negligible level when the ratio and percentage of the west window to the west wall is between 60 and 70 percent.

## 5. Conclusions

_{2}emissions, and optimization was modeled using DesignBuilder, an SVM model, and an ACS algorithm. The application of ACS revealed that if the building includes a brick wall with 40 mm thick insulation, a beam roof with 40 mm thick insulation, three-pane glass, a proportion of north and east windows to the wall in the same direction of 60%, a proportion of the south window to the south wall between 10 and 14%, and a proportion of the west window to the west wall between 35 and 50%, the amount of energy and CO

_{2}is minimal.

_{2}emissions are examined, including the optimal window-to-wall ratios in different building orientations. If the objective function of energy is chosen, the ACS results are in close agreement with the optimization results when the objective function is CO

_{2}concentration. In other words, the optimization of one objective function will result in the optimization of the other. Thus, we will not have to optimize two objectives for this specific problem. Consequently, the policy model for reducing greenhouse gases can also be presented. It is suggested that considering the climate conditions besides the building’s physical features should be evaluate in the model performance in future studies.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

${\mathit{\tau}}_{\mathit{i}\mathit{j}}$ | The pheromone value is on the edge that connects nodes i and j. |

${\mathit{p}}_{\mathit{i}\mathit{j}}^{\mathit{k}}$ | Probability of moving from node i to unvisited node j by ant k. |

${\mathit{\eta}}_{\mathit{i}\mathit{j}}$ | Innovative information to measure the ant’s field of view |

$\mathit{\alpha},\mathit{\beta}$ | Parameters are controls that determine the importance ratio of the value of the ant’s field of view against the pheromone marker on the edge connecting node i and j. |

$\mathit{q}$ | A random parameter uniformly distributed in [0, 1]. |

${\mathit{q}}_{\mathbf{0}}$ | A constant threshold parameter in [0, 1] that determines the importance ratio of mining to exploration. |

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**Figure 1.**Jakarta, Indonesia’s location in the South Pacific Ocean (https://cdn.worlddata.info/pics/countrymaps/IDN.png (accessed on 10 May 2017)).

**Figure 5.**Comparison of the SVM-model-predicted energy consumption for (

**a**) training data and (

**b**) test data, and (

**c**) scatter plot of the SVM and numerical results.

**Figure 6.**Comparison of the SVM-model-predicted CO

_{2}production for (

**a**) training data and (

**b**) test data, and (

**c**) scatter plot of the SVM and numerical results.

**Figure 7.**The results of (

**a**) energy consumption and (

**b**) CO

_{2}production variation in terms of 120 maximum iterations.

Annual Values | Jakarta, Sumatra, Indonesia |
---|---|

Daytime maximum temperature | 31.70 °C |

Daily low temperature | 23.60 °C |

Water temperature | 28.20 °C |

Humidity | 83% |

Precipitation | 2584 mm |

Rain days | 152.4 days |

Hours of sunshine | 1789 h |

**Table 2.**Characteristics regarding the materials utilized for the existing and optimal ceiling and interior and exterior walls.

Building Elements | Materials | Heat Transfer Coefficient (W/m^{2}.k) | Thickness (mm) |
---|---|---|---|

Existing | |||

Reinforced concrete girder | Mosaic | 1.456 | 22 |

Plaster | 45 | ||

Waterproofing | 45 | ||

Concrete | 105 | ||

Block | 250 | ||

Plaster | 45 | ||

Brick wall | 2.650 | 220 | |

Optimal | |||

Concrete Slab | Bituminous waterproofing | 1.520 | 32 |

Plaster | 25 | ||

Lightweight concrete | 40 | ||

Concrete | 250 | ||

Plaster | 20 | ||

Concrete wall | Lightweight concrete | 2.291 | 125 |

Plaster | 22 | ||

Roof insulation | Polystyrene | 0.750 | 35 |

Polystyrene | 0.645 | 40 | |

Wall insulation | Polystyrene | 0.844 | 35 |

Polystyrene | 0.541 | 40 | |

Double-glazed glass | Two layers of glass | 1.562 | 8 |

Argon gas Two layers of | 3 | ||

Window triple glass | transparent glass | 0.656 | 4 |

Low emissivity glass | 1 | ||

Krypton gas | 15 |

Output | Stage | Statistical Index | ||
---|---|---|---|---|

R | R^{2} | RMSE | ||

Energy consumption | Train | 0.921 | 0.874 | 904 (kWh) |

Test | 0.882 | 0.824 | 1012 (kWh) | |

CO_{2} | Train | 0.901 | 0.831 | 1129 (kg) |

Test | 0.863 | 0.799 | 1076 (kg) |

max_it | q_{0} | β | α | ρ | k | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

120 | 0.6 | 0.79 | 0.98 | 1 | 3 | 5 | 1 | 2 | 0.1 | 0.5 | 0.9 | 2 | 5 | 8 |

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## Share and Cite

**MDPI and ACS Style**

Anupong, W.; Muda, I.; AbdulAmeer, S.A.; Al-Kharsan, I.H.; Alviz-Meza, A.; Cárdenas-Escrocia, Y.
Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System. *Sustainability* **2023**, *15*, 3118.
https://doi.org/10.3390/su15043118

**AMA Style**

Anupong W, Muda I, AbdulAmeer SA, Al-Kharsan IH, Alviz-Meza A, Cárdenas-Escrocia Y.
Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System. *Sustainability*. 2023; 15(4):3118.
https://doi.org/10.3390/su15043118

**Chicago/Turabian Style**

Anupong, Wongchai, Iskandar Muda, Sabah Auda AbdulAmeer, Ibrahim H. Al-Kharsan, Aníbal Alviz-Meza, and Yulineth Cárdenas-Escrocia.
2023. "Energy Consumption and Carbon Dioxide Production Optimization in an Educational Building Using the Supported Vector Machine and Ant Colony System" *Sustainability* 15, no. 4: 3118.
https://doi.org/10.3390/su15043118