Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm

Nasruddin, Nasruddin; Sholahudin,; Satrio, Pujo; Mahlia, T.M.I.; Giannetti, Niccolò; Saito, Kiyoshi

doi:10.1016/j.seta.2019.06.002

Cited by 157 publications

(49 citation statements)

References 33 publications

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“…In [128], energy performance of a building, considering different configurations and types of phase change materials, was evaluated by means of multi-objective optimization in five cities of Iran: Tehran, Tabriz, Bandar Abas, Shiraz, and Yazd-each having distinctive climate. In [129], optimization of the HVAC system energy consumption in a building was performed using ANNs and MOGA. The results show that the proposed algorithm has good quality in finding optimum values.…”

Section: Sustainability 2020 12 X For Peer Review 18 Of 38mentioning

confidence: 99%

Sustainability and Optimization: From Conceptual Fundamentals to Applications

2020

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In recent years, both sustainability and optimization concepts have become inseparable developing topics with diverse concepts, elements, and aspects. The principal goal of optimization is to improve the overall sustainability including the environmental sustainability, social sustainability, economic sustainability, and energy resources sustainability through satisfying the objective functions. Therefore, applying optimization algorithms and methods to achieve the sustainable development have significant importance. This paper represents a considerable review on the employed optimization methodologies to sustainability and the sustainable development including sustainable energy, sustainable buildings, and sustainable environment. Since energy optimization is one of the major necessities of sustainability, sustainable development is investigated from the energy perspective. In addition, the concept, definitions, and elements of the sustainability and optimization have been presented, and the review of the optimization metaheuristic algorithms used in recent published articles related to sustainability and sustainable development was carried out. Thus, it is believed that this paper can be appropriate, beneficial, and practical for students, academic researchers, engineers, and other professionals.

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Section: Sustainability 2020 12 X For Peer Review 18 Of 38mentioning

confidence: 99%

Sustainability and Optimization: From Conceptual Fundamentals to Applications

2020

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“…We have simulated the heating system several times on a one-day profile in order to show the performance of each controller on the different configurations (models). To evaluate performance of these network architectures for control, we have used the mean square error (MSE) criteria given by equation (14). The Table 5 represent the performance of each pair controller/model for the three stuctures of neural networks given in the Table 4 by using equation (14).…”

Section: Neural Network For Controlmentioning

confidence: 99%

“…To evaluate performance of these network architectures for control, we have used the mean square error (MSE) criteria given by equation (14). The Table 5 represent the performance of each pair controller/model for the three stuctures of neural networks given in the Table 4 by using equation (14). From Table 5, we can see that the performance of each controller is good for the model it has been trained for and less good for the other models.…”

Section: Neural Network For Controlmentioning

confidence: 99%

“…Different types of controllers of heating systems may be used to optimize the energy efficiency and to ensure a certain level of comfort for the occupants, these controllers are of type proportional-integralderivative (PID) controllers, auto-tuning methods of PID parameters [6,7], fuzzy logic controllers [8], genetic algorithms [9], distributed iterative learning temperature control [10], predictive control [11,12] and other work such as [13,14,15,16,17,18,19,20]. All these methods require adjustments and are generally based on linear model.…”

Section: Introductionmentioning

confidence: 99%

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Supervisory control of building heating system with insulation changes using three architectures of neural networks

Ahmed

Lehouche

Mendil

et al. 2020

Journal of the Franklin Institute

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Until the total energy consumption of the buildings sector reached 40% of the total energy consumed [2]. To be more specific, the cooling and heating framework inside the buildings have the largest share of this consumption, in particular 50% of the power consumption [3]. Thus, reducing energy consumption is a critical issue.…”

Section: Introductionmentioning

confidence: 99%

An occupancy-based strategy employing computer vision for reducing cooling energy consumed in buildings

2021

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The energy expended to cool the occupied areas by air conditioners represents a substantial share of the total energy exhausted in buildings. Therefore, developing strategies to reduce this energy is crucial. One of the preponderance strategies adopted to depreciate energy consumption in buildings is the occupancy-based strategy. In this research, an innovative model was established to achieve the goal of reducing cooling energy consumed in buildings based on occupancy-based combined with a constant temperature setpoint strategy in two phases, and each phase engrosses in 20 days. Phase one is to identify the extent of cooling energy employed according to the use of room occupants and its costs in consumption was 276.01 kWh after completion of this phase. Sequentially, constructing phase two intended to reduce cooling energy consumption by employing an automatic air-conditioner (AC) control strategy relying on an improved human detection algorithm with a 25℃ as temperature setpoint, resulting in 112.45 kWh of consumption. To complement the motives for elaboration, the human detection measurement using you only look once (YOLO) improved by applying pre-processing algorithms to reach an average human detection enhancement of 21.2%. The proposed model results showed that potential savings associated with the embraced strategy decreases by more than anticipated as the amount of reduced energy reached 59% savings.

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Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm

Cited by 157 publications

References 33 publications

Sustainability and Optimization: From Conceptual Fundamentals to Applications

Sustainability and Optimization: From Conceptual Fundamentals to Applications

Supervisory control of building heating system with insulation changes using three architectures of neural networks

An occupancy-based strategy employing computer vision for reducing cooling energy consumed in buildings

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