An optimization model for selecting the optimal green systems by considering the thermal comfort and energy consumption

Kim, Jimin; Hong, Taehoon; Jeong, Jaemin; Koo, Choongwan; Jeong, Kwangbok

doi:10.1016/j.apenergy.2016.02.032

Cited by 89 publications

(24 citation statements)

References 72 publications

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“…From the above works we can see that it is necessary to identify the factors that contribute to increased energy consumption to be able to maintain the desired temperature without increasing its use while heating the building in winter or cooling it in summer. Some of these factors include the location of the building, the state of the building and climate conditions [ 10 ]. Although these factors predetermine the energy use to be greater, the behavior of the people who work in these building also has a major influence.…”

Section: Introductionmentioning

confidence: 99%

Energy Optimization Using a Case-Based Reasoning Strategy

González‐Briones

Prieto

Prieta

et al. 2018

Sensors

123

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At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.

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Section: Introductionmentioning

confidence: 99%

Energy Optimization Using a Case-Based Reasoning Strategy

González‐Briones

Prieto

Prieta

et al. 2018

Sensors

123

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“…The air cooled by green spaces accumulates to form a three-dimensional (3D) urban cool island that can reduce cooling energy demand within and around urban areas [12], [2], [23], [24], [25], [26] and [27]. Thus, green spaces may act as natural air conditioning and save building energy, especially during the hotter summer daytime [28], [29], [30], [31] and [32]. However, most studies to date have only examined the cooling effect of green spaces and changes in air temperature across horizontal scales [12], [23] and [24], while the potential to save energy via 3D (both horizontal and vertical) cooling has not been studied extensively.…”

Section: Introductionmentioning

confidence: 99%

Energy saving potential of fragmented green spaces due to their temperature regulating ecosystem services in the summer

Kong¹,

Sun²,

Liu³

et al. 2016

Applied Energy

100

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Dronova, (2016). Energy saving potential of fragmented green spaces due to their temperature regulating ecosystem services in the summer. Applied Energy. 183, pp.1428-1440. Highlights  The thermal environment was simulated with a validated ENVI-met model.  Daily 3D spatial temperature variation was identified with and without green spaces.  Green spaces below mean building height provided variable cooling benefits.  The temperature reduction through fragmented green spaces was translated into cooling energy equivalents.

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“…In Ref. [22], the multiobjective optimization technique corresponds to the genetic algorithm aimed to select the optimal solar thermal energy systems. The use of multi-objective optimization techniques presents all possible optimal solutions in the form of Pareto front in order to investigate the interaction of objectives.…”

Section: Introductionmentioning

confidence: 99%

Analysis strategy for multi-criteria optimization: Application to inter-seasonal solar heat storage for residential building needs

Launay

Kadoch

Métayer

et al. 2019

Energy

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As energetic systems become more and more complex, it is necessary to develop strategies for analyzing the influence of the parameters as well as their couplings. This approach is even unavoidable when the optimization procedure is based on multi-criteria. In the present work, we propose an analysis strategy for multi-criteria optimization applied to inter-seasonal solar heat storage for residential building energy needs, including heating and domestic hot water. The modeling is based on simplified equations of the components, while keeping the main physical and coupling phenomena. A sensitivity study is applied to the corresponding energetic system in order to identify the most relevant parameters and couplings interacting on the various output objectives. Several simulations are performed to investigate a multiobjective optimization and various figure representations are presented to refine the analysis.

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An optimization model for selecting the optimal green systems by considering the thermal comfort and energy consumption

Cited by 89 publications

References 72 publications

Energy Optimization Using a Case-Based Reasoning Strategy

Energy Optimization Using a Case-Based Reasoning Strategy

Energy saving potential of fragmented green spaces due to their temperature regulating ecosystem services in the summer

Analysis strategy for multi-criteria optimization: Application to inter-seasonal solar heat storage for residential building needs

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