# Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms

^{1}

^{2}

^{3}

^{4}

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## Abstract

**:**

## 1. Introduction

_{2}concentration. The ventilation system keeps the CO

_{2}concentration as low as possible to keep the occupants in their comfort zone inside the building [6]. These three parameters (temperature, illumination, and air quality) are considered to maintain the comfort level inside the building according to the users’ demands. Three parameters in our work to maintain the IEQ of the building depending on the users’ demands have been considered. The methodology adopted in this work followed the standard proposed by the authors in [1,2,3,7,8,9]. The authors in all of the above research works have used the air temperature as the input of the temperature parameter, because air temperature measurements are adequately easy in implementation. For reading the air temperature, the temperature sensor only reads the values, and no further computation is required.

## 2. Literature Review

## 3. Proposed Approach

#### 3.1. Proposed AI Algorithm

_{0}indicates the light intensity emitted at the light source point. In such a case, γ is the absorption coefficient of the medium, I is the light intensity from distance r, which is then calculated by the Equation (2).

_{0}is the attractiveness at the distance r = 0. While the separation between two fireflies’ x

_{i}and y

_{j}is the Euclidean distance that can be calculated by Equation (4).

- An initial population is created for the GA using the standard FA population.
- The fitness function for user comfort is computed using Equation (13).
- The best individuals are selected using Roulette wheel, Rank, or tournament selection. In our work, rank-based selection was used.
- One-point crossover of the selected individuals is performed.
- Offsprings are generated after the crossover.
- The mutation operation is performed.
- The above steps are repeated for the specified number of iterations.
- When the termination criterion is met, the best-fitted chromosomes are selected.
- The best chromosomes obtained represent the value of the maximum comfort index.

#### 3.2. Comfort Index

_{s}represents the user-set air quality parameter value.

_{u}is the user-set temperature, I

_{u}is the user-set illumination, and A

_{u}is the user-set air quality value. After the computation of comfort, the required power for maintaining the comfort is supplied by the controller agent. The total power required is the combination of the electric power required for the three parameters, as shown in Equation (14) [30].

- ${E}_{TOTAL}$ = total required power,
- ${E}_{T}$ = power required for temperature comfort,
- ${E}_{I}$ = power required for illumination comfort, and
- ${E}_{A}$ = power required for air quality comfort.

_{TOTAL}≤ ${E}_{max},$ where ${E}_{max}$ is the maximum power that is provided by the power source. In order to achieve the required temperature, illumination, and air quality comfort, the fuzzy controllers are used. The coordinator agent uses the outputs of the fuzzy controllers to change the statuses of the actuators concerned. According to the authors of [7], the power required for maintaining the temperature (${E}_{T}$), illumination (${E}_{I}$), and air quality (${E}_{A}$) comfort is given by Equations (15)–(17), respectively.

_{T}= 5.655 ∗ T + 2.961

_{I}= 4.428 ∗ sin (0.9603 ∗ I − 0.4234)

#### 3.3. Fuzzy Controllers

#### 3.3.1. Temperature Fuzzy Controller

- If ($er{r}_{1}$ = = NH), then $P{R}_{1}$ = PR
_{1}NH - If ($er{r}_{1}$ = = NM), then $P{R}_{1}$ = PR
_{1}NM - If ($er{r}_{1}$ = = NL), then $P{R}_{1}$ = PR
_{1}NL - If ($er{r}_{1}$ = = ZE), then $P{R}_{1}$ = PR
_{1}ZE - If ($er{r}_{1}$ = = PL), then $P{R}_{1}$ = PR
_{1}PL - If ($er{r}_{1}$ = = PM), then $P{R}_{1}$ = PR
_{1}PM - If ($er{r}_{1}$ = = PH), then $P{R}_{1}$ = PR
_{1}PH

_{1}is the error between the external environmental temperature and the FA-GA-optimized temperature calculated by Euclidean distance, and PR

_{1}represents the output power generated for controlling the actuator status. The output difference is the error, and it will be an input to the temperature fuzzy controller. Based on that output error, the energy PR

_{1}(required power 1) will be generated by the temperature fuzzy controller for providing it to the heating and cooling actuators. NH is the least output error for the external environmental temperature and FA-GA-improved temperature, tailed by NM, NL, ZE, PL, PM, and PH. Therefore, it can be moved from NH towards PH while the difference increases and vice versa. Similarly, the desired power ($P{R}_{1}$) for a heating and cooling controlling system is the least ($P{R}_{1}$ = $P{R}_{1}NH$) for the error difference NH and the highest error difference $PH$, i.e., $P{R}_{1}$ = $P{R}_{1}PH$. So, PH is the least error difference between the external environmental temperature and the FA-GA-optimized temperature. While $P{R}_{1}NH$ is the least power required for the heating and cooling system, and PR

_{1}PH is the highest power required for the heating and cooling system control.

#### 3.3.2. Illumination Fuzzy Controller

- If ($er{r}_{2}$ = = HS), then ${PR}_{2}$ = ${PR}_{2}$HS
- If ($er{r}_{2}$ = = MS), then ${PR}_{2}$ = ${PR}_{2}$MS
- If ($er{r}_{2}$ = = BS), then PR
_{2}= $P{R}_{2}BS$ - If ($er{r}_{2}$ = = OK), then PR
_{2}= $P{R}_{2}OK$ - If ($er{r}_{2}$ = = SH), then PR
_{2}= $P{R}_{2}SH$ - If ($er{r}_{2}$ = = H), then PR
_{2}= $P{R}_{2}H$

_{2}is the required power for illumination, and PR

_{2}with input variable membership function labels in the output variable indicate the required power for the corresponding input membership function.

#### 3.3.3. Air Quality Fuzzy Controller

- If ($er{r}_{3}$ = = LOW), then $P{R}_{3}$ = $P{R}_{3}LOW$
- If ($er{r}_{3}$ = = OK), then $P{R}_{3}$ = $P{R}_{3}OK$
- If ($er{r}_{3}$ = = SH), then $P{R}_{3}$ = $P{R}_{3}SH$
- If ($er{r}_{3}$ = = LH), then $P{R}_{3}$ = $P{R}_{3}LH$
- If ($er{r}_{3}$ = = HIGH), then $P{R}_{3}$ = $P{R}_{3}HIGH$

_{3}is the required power for the air quality, and PR

_{3}with input variable membership functions labels in the output variable indicate the required power for the corresponding input membership function.

#### 3.4. Coordinator

#### 3.5. Actuators

## 4. Experimental Setup and Discussion

#### 4.1. Parameter Optimizations

#### 4.2. Temperature Control System

#### 4.3. Illumination Control System

#### 4.4. Air Quality Control System

_{2}concentration) for all of the considered approaches is shown in Table 6. The performance and efficiency of all the algorithms are measured in terms of the minimum values for these differences. These values are given in their standard forms, but when considered in the computations of power consumption and comfort index, these are taken in their absolute form. The major aim of all the optimization algorithms is to reduce the absolute values of these parameters. The table clearly indicates that this aim has been achieved by the proposed model in a better way as comparatively to the other optimization algorithms applied in their standard formats. The efficiency in the result of minimizing these values by the proposed model is the result of hybridizing two optimization algorithms in their conventional procedures. For all other optimization models, many fluctuations can be observed in these values. In some cases, one technique may outperform the other, whereas, in other cases, the result may be the opposite. Since these values are used in turn for calculating the air quality power consumption and the occupant’s comfort, the minimum values for this will result in less power consumption and higher user comfort, as outlined in the mathematical formula used for computing these values. The proposed model has been adequately proved to be the most powerful as compared to the other standard models. The result behind this efficiency is their combination of two standard models that combine the advantages of both the techniques while eliminating their disadvantages.

## 5. Statistical Analysis of All Approaches

## 6. Conclusions and Future Work

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Ali, S.; Kim, D.H. Optimized Power Control Methodology Using Genetic Algorithm. Wirel. Pers. Commun.
**2015**, 83, 493–505. [Google Scholar] [CrossRef] - Ali, S.; Kim, D.H. Effective and Comfortable Power Control Model Using Kalman Filter for Building Energy Management. Wirel. Pers. Commun.
**2013**, 73, 1439–1453. [Google Scholar] [CrossRef] - Wang, Z.; Yang, R.; Wang, L. Multi-Agent Control System with Intelligent Optimization for Smart and Energy-Efficient Buildings. In Proceedings of the IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society, Piscataway, NJ, USA, 7–10 November 2010; pp. 1144–1149. [Google Scholar]
- Dounis, A.I.; Caraiscos, C. Advanced Control Systems Engineering for Energy and Comfort Management in a Building Environment—A Review. Renew. Sustain. Energy Rev.
**2009**, 13, 1246–1261. [Google Scholar] [CrossRef] - Wang, Z.; Yang, R.; Wang, L. Multi-Agent Intelligent Controller Design for Smart and Sustainable Buildings. In Proceedings of the 2010 IEEE International Systems Conference, Piscataway, NJ, USA, 5–8 April 2010; pp. 277–282. [Google Scholar]
- Emmerich, S.J.; Persily, A.K. State-of-the-Art Review of CO2 Demand Controlled Ventilation Technology and Application; Diane Publishing: Collingdale, PA, USA, 2003. [Google Scholar]
- Wahid, F.; Ismail, L.H.; Ghazali, R.; Aamir, M. An Efficient Artificial Intelligence Hybrid Approach for Energy Management in Intelligent Buildings. KSII Trans. Internet Inf. Syst.
**2019**, 13, 5904–5927. [Google Scholar] [CrossRef] - Wahid, F.; Ghazali, R.; Ismail, L.H. Improved Firefly Algorithm Based on Genetic Algorithm Operators for Energy Efficiency in Smart Buildings. Arab. J. Sci. Eng.
**2019**, 44, 4027–4047. [Google Scholar] [CrossRef] - Shaikh, P.H.; Nor, N.B.M.; Nallagownden, P.; Elamvazuthi, I.; Ibrahim, T. Intelligent Multi-Objective Control and Management for Smart Energy Efficient Buildings. Int. J. Electr. Power Energy Syst.
**2016**, 74, 403–409. [Google Scholar] [CrossRef] - Alfano, F.R.D.; Olesen, B.W.; Palella, B.I.; Riccio, G. Thermal Comfort: Design and Assessment for Energy Saving. Energy Build.
**2014**, 81, 326–336. [Google Scholar] [CrossRef] - EN Standard 16798-1. Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics; Module M1-6—European Committee for Standardization: Brussels, Belgium, 2019. [Google Scholar]
- Fanger, P.O. Thermal comfort. Analysis and applications in environmental engineering. In Thermal Comfort. Analysis and Applications in Environmental Engineering; Danish Technical Press: Copenhagen, Denmark, 1970. [Google Scholar]
- Huang, L.; Zhu, Y.; Ouyang, Q.; Cao, B. A Study on the Effects of Thermal, Luminous, and Acoustic Environments on Indoor Environmental Comfort in Offices. Build. Environ.
**2012**, 49, 304–309. [Google Scholar] [CrossRef] - ASHRAE. “Thermal Environmental Conditions for Human Occupancy”, ANSI/ASHRAE 55; American Society of Heating, Refrigerating and Air Conditioning Engineers: Atlanta, GA, USA, 1992. [Google Scholar]
- ISO Standard 7730. Ergonomics of the Thermal Environment—Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort; International Organization for Standardization: Geneva, Switzerland, 2005. [Google Scholar]
- Alfano, F.R.D.; Palella, B.I.; Riccio, G.; Toftum, J. Fifty Years of Fanger’s Equation: Is there Anything to Discover Yet? Int. J. Ind. Ergon.
**2018**, 66, 157–160. [Google Scholar] [CrossRef] - Yang, X.-S. Firefly Algorithm. Eng. Optim.
**2010**, 20, 221–230. [Google Scholar] [CrossRef] - Fister, I.; Yang, X.S.; Fister, D. Firefly Algorithm: A Brief Review of the Expanding Literature. In Cuckoo Search and Firefly Algorithm; Springer: Cham, Switzerland, 2014; pp. 347–360. [Google Scholar]
- Wetter, M.; Bonvini, M.; Nouidui, T.S. Equation-Based Languages—A New Paradigm for Building Energy Modeling, Simulation and Optimization. Energy Build.
**2016**, 117, 290–300. [Google Scholar] [CrossRef][Green Version] - Wang, Z.; Wang, L.; Dounis, A.I.; Yang, R. Multi-Agent Control System with Information Fusion Based Comfort Model for Smart Buildings. Appl. Energy
**2012**, 99, 247–254. [Google Scholar] [CrossRef] - Bluyssen, P.M.; Aries, M.B.C.; Van Dommelen, P. Comfort of Workers in Office Buildings: The European HOPE Project. Build. Environ.
**2011**, 46, 280–288. [Google Scholar] [CrossRef] - Marino, C.; Nucara, A.; Pietrafesa, M. Proposal of Comfort Classification Indexes Suitable for Both Single Environments and Whole Buildings. Build. Environ.
**2012**, 57, 58–67. [Google Scholar] [CrossRef] - Korkas, C.D.; Baldi, S.; Michailidis, I.; Kosmatopoulos, E.B. Intelligent Energy and Thermal Comfort Management in Grid-Connected Microgrids with Heterogeneous Occupancy Schedule. Appl. Energy
**2015**, 149, 194–203. [Google Scholar] [CrossRef] - Korkas, C.D.; Baldi, S.; Michailidis, I.; Kosmatopoulos, E.B. Occupancy-Based Demand Response and Thermal Comfort Optimization in Microgrids with Renewable Energy Sources and Energy Storage. Appl. Energy
**2016**, 163, 93–104. [Google Scholar] [CrossRef] - Delgarm, N.; Sajadi, B.; Kowsary, F.; Delgarm, S. Multi-Objective Optimization of the Building Energy Performance: A Simulation-Based Approach by Means of Particle Swarm Optimization (PSO). Appl. Energy
**2016**, 170, 293–303. [Google Scholar] [CrossRef] - Penna, P.; Prada, A.; Cappelletti, F.; Gasparella, A. Multi-Objectives Optimization of Energy Efficiency Measures in Existing Buildings. Energy Build.
**2015**, 95, 57–69. [Google Scholar] [CrossRef] - Yu, W.; Li, B.; Jia, H.; Zhang, M.; Wang, D. Application of Multi-Objective Genetic Algorithm to Optimize Energy Efficiency and Thermal Comfort in Building Design. Energy Build.
**2015**, 88, 135–143. [Google Scholar] [CrossRef] - Lu, Y.; Wang, S.; Zhao, Y.; Yan, C. Renewable Energy System Optimization of Low/Zero Energy Buildings Using Single-Objective and Multi-Objective Optimization Methods. Energy Build.
**2015**, 89, 61–75. [Google Scholar] [CrossRef] - Carlucci, S.; Cattarin, G.; Causone, F.; Pagliano, L. Multi-Objective Optimization of a Nearly Zero-Energy Building Based on Thermal and Visual Discomfort Minimization Using a Non-Dominated Sorting Genetic Algorithm (NSGA-II). Energy Build.
**2015**, 104, 378–394. [Google Scholar] [CrossRef][Green Version] - Fayaz, M.; Kim, D. Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic. Energies
**2018**, 11, 161. [Google Scholar] [CrossRef][Green Version] - Nguyen, T.A.; Aiello, M. Energy Intelligent Buildings Based on User Activity: A Survey. Energy Build.
**2013**, 56, 244–257. [Google Scholar] [CrossRef][Green Version] - Li, S.; Chen, H.; Wang, M.; Heidari, A.A.; Mirjalili, S. Slime Mould Algorithm: A New Method for Stochastic Optimization. Future Gener. Comput. Syst.
**2020**. [Google Scholar] [CrossRef] - Faramarzi, A.; Heidarinejad, M.; Stephens, B.; Mirjalili, S. Equilibrium Optimizer: A Novel Optimization Algorithm. Knowl. Based Syst.
**2020**, 191, 105190. [Google Scholar] [CrossRef] - Granite, E.J.; Hargis, R.A.; Pennline, H.W. Sorbents for Mercury Removal from Flue Gas (No. DOE/FETC/TR-98-01); Federal Energy Technology Center-Pittsburgh (FETC-PGH): Pittsburgh, PA, USA, 1998.
- Lobo, F.G.; Goldberg, D.E.; Pelikan, M. Time Complexity of Genetic Algorithms on Exponentially Scaled Problems. In Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation, Las Vegas, NV, USA, 8 July 2000; pp. 151–158. [Google Scholar]
- Eberhart, R.; Kennedy, J. Particle Swarm Optimization. In Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 27 November–1 December 1995; Volume 4. [Google Scholar]

**Figure 10.**(

**a**) Applied rule for a single value based on the illumination fuzzy controller. (

**b**) The power utilized with respect to illumination.

Notation | Description |
---|---|

T | Environmental Temperature |

Ts | User Set Temperature |

I | Environmental Illumination |

Is | User Set Illumination |

A | Environmental Air Quality |

As | User Set Air Quality |

CI | Comfort Index |

err1 | Error Difference between Environmental Temperature and User Set Temperature |

err2 | Error Difference between Environmental Illumination and User Set Illumination |

err3 | Error Difference Environmental Temperature and User Temperature |

Prr1 | Preference Parameters for Temperature |

Prr2 | Preference Parameters for Illumination |

Prr3 | Preference Parameters for Air Quality |

E_{TOTAL} | Total Required Power |

E_{T} | Power Required for Temperature |

E_{I} | Power Required to Illumination |

E_{A} | Power Required for Air Quality |

E_{mx} | Maximum Supplied Power |

GA | Genetic Algorithm |

FA | Firefly Algorithm |

ABC | Artificial Bee Colony |

ACO | Artificial Ant Colony |

DE | Differential Equation |

Algorithms | Parameters | Values |
---|---|---|

FA | Iterations | 200 |

Population Size | 150 | |

Gamma | 1 | |

Beta | 2 | |

Alpha | 0.2 | |

GA | Iterations | 200 |

Population Size | 150 | |

Type of Crossover | One-Point Crossover | |

Probability of Crossover | 0.5 | |

Mutation Rate | 0.1 | |

Proposed Method | Iterations | 200 |

Population Size | 150 | |

Gamma | 1 | |

Beta | 2 | |

Alpha | 0.2 | |

Type of Crossover | One-Point Crossover | |

Probability of Crossover Mutation Rate | 0.5 0.1 |

Parameter | Unit | User Lower Limit | User Upper Limit | Central Point | Environment Lower Limit | Environment Upper Limit |
---|---|---|---|---|---|---|

Temperature | Kelvin | 68.0 | 78.0 | 73.0 | 60.0 | 85.0 |

Illumination | Lux | 730.0 | 880.0 | 800.0 | 700.0 | 920.0 |

Air Quality | CO_{2} | 730.0 | 880.0 | 800.0 | 700.0 | 920.0 |

GA | FA | ABC | ACO | PSO | DE | FA-GA |
---|---|---|---|---|---|---|

10.55 | 10.55 | 8.88 | 10.34 | 9.34 | 10.66 | 9.45 |

4.55 | 5.87 | 5.54 | 4.54 | 4.45 | 4.66 | 3.8 |

2.965 | 2.65 | 2.665 | 1.996 | 3.023 | 1.998 | 1.75 |

4.564 | 4.453 | 3.301 | 4.454 | 3.576 | 4.343 | 2.96 |

3.564 | 3.132 | 2.665 | 3.564 | 3.343 | 2.554 | 1.45 |

6.564 | 6.665 | 5.795 | 7.476 | 7.342 | 7.554 | 6.32 |

8.665 | 8.178 | 8.276 | 7.665 | 7.4554 | 8.554 | 7.34 |

−5.24 | −4.35 | −5.11 | −2.01 | −3.21 | −4.32 | −3.2 |

−3.57 | −3.21 | −3.57 | −3.44 | −3.21 | −4.14 | −3.0 |

−4.68 | −4.32 | −5.33 | −4.11 | −4.55 | −4.11 | −3.4 |

−7.13 | −7.23 | −6.47 | −7.24 | −7.11 | −5.77 | −5.1 |

−3.44 | −3.22 | −4.10 | −3.78 | −3.546 | −4.13 | −3.1 |

−2.57 | −2.32 | −2.57 | −3.24 | −2.103 | −3.13 | −1.6 |

−5.55 | −6.01 | −5.67 | −5.35 | −5.446 | −5.13 | −4.5 |

−5.13 | −4.99 | −5.13 | −5.02 | −5.436 | −4.55 | −3.1 |

−4.24 | −3.54 | −3.76 | −3.13 | −4.103 | −3.35 | −2.5 |

4.54 | 4.675 | 3.554 | 3.76 | 3.453 | 4.554 | 2.87 |

7.43 | 6.43 | 7.132 | 7.45 | 5.95 | 7.55 | 5.45 |

3.554 | 3.546 | 4.44 | 3.45 | 2.69 | 3.65 | 2.5 |

4.235 | 3.443 | 3.99 | 3.34 | 3.56 | 2.965 | 2.12 |

10.55 | 10.55 | 8.88 | 10.3 | 9.3432 | 10.666 | 9.45 |

GA | FA | ABC | ACO | PSO | DE | FA-GA |
---|---|---|---|---|---|---|

41.77 | 37.87 | 41.8 | 33.76 | 34.65 | 32.76 | 26.87 |

43.655 | 35.77 | 30.8 | 36.76 | 24.76 | 30.56 | 21.98 |

46.65 | 28.87 | 33.7 | 29.76 | 29.87 | 25.87 | 22.65 |

25.65 | 26.76 | 26.8 | 37.87 | 37.76 | 33.54 | 20.98 |

52.76 | 44.87 | 48.8 | 53.67 | 63.87 | 60.45 | 44.76 |

40.87 | 40.87 | 33.8 | 34.45 | 47.54 | 50.78 | 27.76 |

28.655 | 20.98 | 24.4 | 29.76 | 38.66 | 34.55 | 15.87 |

39.76 | 29.98 | 39.6 | 39.67 | 44.87 | 42.78 | 30.44 |

−57.22 | −58.1 | −60.3 | −62.3 | −67.3 | −68.2 | −46.3 |

−43.46 | −46.1 | −51.6 | −56.3 | −46.3 | −39.5 | −35.13 |

−45.33 | −39.3 | −39.2 | −46.5 | −43.2 | −41.3 | −28.13 |

−49.22 | −44.4 | −48.2 | −37.3 | −47.1 | −48.3 | −27.46 |

−52.23 | −45.2 | −47.4 | −53.6 | −53.4 | −51.4 | −40.43 |

−50.24 | −56.1 | −54.4 | −50.3 | −58.3 | −56.2 | −38.24 |

−58.12 | −51.1 | −60.3 | −51.2 | −49.1 | −55.5 | −45.56 |

−43.3 | −41.1 | −43.1 | −46.4 | −43.2 | −43.2 | −32.11 |

18.87 | 14.8 | 17.8 | 23.6 | 25.87 | 16.8 | 9.87 |

46.87 | 44.7 | 48.7 | 42.8 | 41.23 | 47.4 | 32.89 |

33.87 | 31.7 | 34.8 | 30.8 | 33.98 | 37.5 | 24.45 |

25.54 | 30.6 | 23.6 | 26.9 | 39.43 | 31.87 | 18.65 |

24.877 | 23.98 | 24.45 | 26.43 | 33.56 | 25.87 | 16.565 |

GA | FA | ABC | ACO | PSO | DE | FA-GA |
---|---|---|---|---|---|---|

138.6 | 142 | 134.8 | 134.8 | 135.7 | 125.8 | 121.8 |

76.76 | 81.87 | 75.87 | 75.98 | 75.87 | 64.87 | 56.87 |

92.76 | 97.87 | 91.87 | 100.8 | 96.87 | 94.76 | 80.75 |

110.4 | 103.8 | 106.8 | 103.8 | 109.8 | 102.7 | 91.65 |

153.6 | 148.9 | 145.8 | 155.8 | 150.6 | 151.6 | 132.7 |

128.8 | 132.8 | 130.5 | 129.8 | 122.8 | 120.6 | 113.8 |

80.76 | 77.87 | 75.87 | 80.76 | 80.67 | 75.76 | 61.65 |

−73.1 | −66.1 | −69.1 | −69.1 | −74.1 | −68.2 | −57.2 |

−71.0 | −71.3 | −71.1 | −68.2 | −72.5 | −69.5 | −55.1 |

−122 | −107 | −103 | −103 | −108 | −106. | −94.2 |

−50.1 | −48.2 | −55.1 | −52.0 | −45.0 | −49.4 | −37.3 |

−78.1 | −81.2 | −78.2 | −89.1 | −81.5 | −76.1 | −66.3 |

−104 | −95.1 | −95.1 | −99.2 | −98.2 | −102 | −85.2 |

−106 | −110 | −109 | −116 | −115 | −119 | −98.2 |

−127 | −113 | −122 | −125 | −124 | −123 | −109 |

−74.2 | −79.1 | −83.1 | −78.1 | −76.1 | −71.1 | −63.2 |

131.7 | 137.8 | 132.8 | 140.7 | 136.8 | 128.8 | 121.8 |

77.87 | 74.87 | 77.98 | 80.87 | 79.87 | 66.87 | 60.76 |

118.9 | 111.7 | 116.9 | 113.8 | 120.5 | 115.8 | 102.8 |

96.8 | 96.87 | 108.8 | 105. | 103.9 | 97.87 | 86.6 |

97.8 | 100.7 | 104.8 | 95.8 | 102.7 | 100.87 | 82.87 |

Parameters | Features | GA | FA | ABC | ACO | PSO | DE | FA-GA |
---|---|---|---|---|---|---|---|---|

Temperature Power Consumption | Minimum | 2.16543 | 1.86555 | 2.4365 | 2.5324 | 2.1543 | 2.2239 | 1.26734 |

Maximum | 8.43356 | 8.57644 | 10.265 | 9.5467 | 9.5654 | 8.4578 | 6.1734 | |

Average | 3.86755 | 4.59765 | 4.5431 | 5.3245 | 3.9786 | 4.1287 | 3.4167 | |

Total | 187.79866 | 192.4534 | 193.65 | 187.543 | 182.565 | 179.534 | 159.6234 | |

Illumination Power Consumption | Minimum | 1.85654 | 1.65445 | 1.854 | 1.59557 | 1.7564 | 1.6784 | 1.1785 |

Maximum | 6.5334 | 6.8324 | 7.213 | 7.5684 | 6.9856 | 6.3905 | 5.16575 | |

Average | 4.2554 | 4.155644 | 3.754 | 4.1456 | 4.3913 | 4.1845 | 3.2167 | |

Total | 195.3545 | 172.3546 | 196.43 | 184.6741 | 177.653 | 183.597 | 152.5198 | |

Air Quality Power Consumption | Minimum | 2.3243 | 1.63445 | 1.8567 | 2.03478 | 1.7859 | 1.6819 | 1.1798 |

Maximum | 7.9433 | 7.5465 | 7.6456 | 8.1206 | 6.8423 | 7.1165 | 5.2344 | |

Average | 5.57654 | 5.12544 | 4.3246 | 5.1408 | 4.8948 | 5.2109 | 3.1155 | |

Total | 249.8548 | 237.2546 | 218.34 | 227.5213 | 229.643 | 231.934 | 197.4466 | |

Total Power Consumption | Minimum | 5.84535 | 7.435462 | 7.4356 | 6.9445 | 6.4786 | 6.7659 | 4.3453 |

Maximum | 14.4537 | 28.3456 | 23.546 | 16.8976 | 17.7812 | 20.376 | 13.8335 | |

Average | 11.3432 | 13.3456 | 12.423 | 11.5987 | 12.1987 | 13.238 | 9.6687 | |

Total | 589.453 | 561.6376 | 594.56 | 577.534 | 568.385 | 571.431 | 523.3853 | |

Comfort Index | Minimum | 0.94321 | 0.94534 | 0.9356 | 0.94768 | 0.94987 | 0.94167 | 0.95734 |

Maximum | 0.97435 | 0.96435 | 0.96435 | 0.96843 | 0.96871 | 0.96731 | 0.9873 | |

Average | 0.95564 | 0.95767 | 0.9534 | 0.95165 | 0.9590 | 0.94956 | 0.9658 |

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**MDPI and ACS Style**

Wahid, F.; Fayaz, M.; Aljarbouh, A.; Mir, M.; Aamir, M.; Imran. Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms. *Energies* **2020**, *13*, 4363.
https://doi.org/10.3390/en13174363

**AMA Style**

Wahid F, Fayaz M, Aljarbouh A, Mir M, Aamir M, Imran. Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms. *Energies*. 2020; 13(17):4363.
https://doi.org/10.3390/en13174363

**Chicago/Turabian Style**

Wahid, Fazli, Muhammad Fayaz, Ayman Aljarbouh, Masood Mir, Muhammad Aamir, and Imran. 2020. "Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms" *Energies* 13, no. 17: 4363.
https://doi.org/10.3390/en13174363