Network-based energy supply optimal system in the condition where both heating and cooling are required simultaneously in a swing season

Ahn, Jonghoon; Chung, Dae Hun; Cho, Soolyeon

doi:10.1080/17508975.2017.1328657

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…Various design scenarios for indoor thermal conditions derived from architectural components, thermal conditions, and users' characteristics were considered to define much more reliable calculation for the PMV levels. By use of modified genetic algorithms derived from experimental data, several functions for the ambiguous situations were developed to maintain the level of buildings' energy performance [25][26][27].…”

Section: Building Thermal Controlsmentioning

confidence: 99%

Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area

Ahn

2020

Sustainability

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For the sustainable use of building spaces, various methods have been studied to satisfy specific conditions required by the characteristics of space types and the energy use in operation. However, several effective control approaches adopting the latest statistical tools may have problems such as higher control precision increases energy consumption, or lower energy consumption decreases their control precision. This study proposes an optimized model to reach the indoor set-point temperature by controlling the amount of heating supply air and its temperature and investigates the efficiency of an adaptive controller to maintain indoor thermal comfort within setting ranges. In the consistency of the comfort level, the fuzzy logic controller was found to be 1.76% and the artificial neural network controller to be 17.83%, respectively, more efficient than the conventional thermostat. In addition, for energy use efficiency, both of the controllers were confirmed to be over 3.0% more efficient. Consequently, the network-based controller with the adaptive controller checking comfort levels effectively works to improve both energy efficiency and thermal comfort. This improvement can be significant in places such as commercial high-rises, large hospitals, and data centers where many spaces are intensively woven with appropriate thermal environments to maintain users’ workability.

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Section: Building Thermal Controlsmentioning

confidence: 99%

Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area

Ahn

2020

Sustainability

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“…In several studies, many complex problems, because the number of combinations increases exponentially with just a few variables, can be investigated using the Artificial Neural Network (ANN). Its algorithm helped to solve complex calculations for analyzing the effects of day-lighting, infiltration and ventilation using small gaps, and latent heat of living, which require time-consuming processes to organize data and find their hidden correlations [14,15]. In addition to the control algorithms mixing the PID, FIS, and ANN models, specific differential functions dealing with the amount of opening for valves or dampers were tested by combining experimental models and ANN algorithms, which react to different demands at specific geometries in buildings or weather conditions [16][17][18].…”

Section: Building Thermal Controlsmentioning

confidence: 99%

Comparative Analyses of Energy Efficiency between on-Demand and Predictive Controls for Buildings’ Indoor Thermal Environment

Sung

Ahn

2020

Energies

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Advanced thermal control technologies have been continuously developed to complement conventional models and algorithms to improve their performance regarding control accuracy and energy efficiency. This study analyses the strengths and weaknesses of simultaneous controls for the amount of air and its temperature by use of on-demand and predictive control strategies responding to two different outdoor conditions. The framework performs the comparative analyses of an on-demand model, which reacts immediately to indoor conditions, and a predictive model, which provides reference signals derived from data learned. Two models are combined to make a comparison of how much more efficient the combined model operates than each model when abnormal situations occur. As a result, when the two models are combined, its efficiency improves from 20.0% to 33.6% for indoor thermal dissatisfaction and from 13.0% to 44.5% for energy use, respectively. This result implies that in addition to creating new algorithms to cope with any abnormal situation, combining existing models can also be a resource-saving approach.

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“…The energy industry is the material basis for modernization, and establishing an energy supply network with high security and high reliability should be a core development strategy, whether in a developed or developing country [1][2][3][4]. Improving the energy use efficiency of the energy system is of great significance to the development of the national energy economy and the improvement of the national economic competitiveness [5][6][7][8][9]. Currently, the analysis of the energy network with various forms of heat-work conversion is mainly based on the theory of thermodynamic analysis.…”

Section: Introductionmentioning

confidence: 99%

Construction and Optimization of a Thermo-economic Cost Analysis Model for the Complex Energy Network

Zhang¹,

Wang²

2021

IJHT

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Improving the energy use efficiency of the energy system is of great significance to the development of the national energy economy and the improvement of the national economic competitiveness. The existing domestic research on thermo-economic costs is insufficient. For example, there is no research on the allocation of thermo-economic costs and on the complex energy network with multiple energy outputs. Therefore, this paper reconstructs and optimizes the thermo-economic cost analysis model for the complex energy network. First, the thermo-economic cost model for each sub-network and that for each energy output of the complex energy network were established, and the structure block diagram of the distributed thermo-economic cost allocation model for the complex energy network was given. Then, a local-global decomposition optimization method was proposed for the complex energy network to achieve the thermo-economic optimization of the complex energy network. The experimental results proved the effectiveness of the proposed algorithm.

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Network-based energy supply optimal system in the condition where both heating and cooling are required simultaneously in a swing season

Cited by 6 publications

References 30 publications

Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area

Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area

Comparative Analyses of Energy Efficiency between on-Demand and Predictive Controls for Buildings’ Indoor Thermal Environment

Construction and Optimization of a Thermo-economic Cost Analysis Model for the Complex Energy Network

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