scholarly journals Thermal Control Processes by Deterministic and Network-Based Models for Energy Use and Control Accuracy in a Building Space

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 385
Author(s):  
Jonghoon Ahn
Keyword(s):  
Thermal Comfort ◽  
Energy Use ◽  
Deterministic Model ◽  
Learning Algorithm ◽  
Fuzzy Model ◽  
Weather Condition ◽  
Thermal Control ◽  
Comfort Level ◽  
Deterministic Models ◽  
Climate Conditions

Various control approaches for building thermal controls have been studied to improve the energy use which determines a large part of the spatial thermal quality. This research compares the performance of deterministic models and a network-based model to examine the aspects of both energy consumption and thermal comfort. The single-switch deterministic model immediately responds to indoor thermal conditions, but the network-based model sends better-fit signals derived from learned data reflecting seven different climate conditions. As a result, the network-based model improves the thermal comfort level by about 6.1% to 9.4% and the energy efficiency by about 1.8% to 39.5% as compared to a thermostat and a fuzzy model. In the case of a specific weather condition, it can be confirmed that the process of finding efficient control values based on the network-based learning algorithm is more efficient than the conventional deterministic models.

scholarly journals Abatement of the Increases in Cooling Energy Use during a Period of Intense Heat by a Network-Based Adaptive Controller

2021 ◽  
Vol 13 (3) ◽  
pp. 1353 ◽  
Author(s):  
Jonghoon Ahn
Keyword(s):  
Energy Efficiency ◽  
Thermal Comfort ◽  
Energy Use ◽  
Deterministic Model ◽  
Thermal Conditions ◽  
Adaptive Controller ◽  
High Energy ◽  
Comfort Level ◽  
Indoor Environmental Quality ◽  
Comfort Indices

Various methods to control thermal conditions of building spaces have been developed to investigate their performances of energy use and thermal comfort in the system levels. However, the high control precision used in several studies dealing with data-driven methods may cause energy increases and the high energy efficiency may be disadvantageous for maintaining indoor environmental quality. This study proposes a model that optimizes the supply air condition to effectively reach the setting values by two-way controls of the supply air conditions. In such a process, if the results of the thermal comfort level are outside the range of the initial setting values, an adaptive model starts to work to send additional signals to adjust the set-point temperature. In order to assess its efficiency, the conventional thermostat model and fuzzy deterministic model are adopted as comparators. Comparing the results of the proposed network-based model with conventional control models, an improved control performance from 15.5% to 29.3% in thermal comfort indices was identified, as well as an over 30% improvement in energy efficiency. As a consequence, the network-based adaptive control rule supervising thermal comfort indices properly operates to abate increases in its energy use without compromising its thermal comfort. This performance can be significant in places where many spaces are woven at high density, and in situations where better thermal comfort can increase users’ workability and productivity.

scholarly journals Improvement of the Performance Balance between Thermal Comfort and Energy Use for a Building Space in the Mid-Spring Season

2020 ◽  
Vol 12 (22) ◽  
pp. 9667
Author(s):  
Jonghoon Ahn
Keyword(s):  
Energy Consumption ◽  
Thermal Comfort ◽  
Energy Use ◽  
Learning Algorithm ◽  
Adaptive Controller ◽  
Comfort Level ◽  
Adaptive Model ◽  
Comfort Levels ◽  
Proposed Model ◽  
High Consistency

In thermal controls in buildings, recent statistical and data-driven approaches to optimize supply air conditions have been examined in association with several types of building spaces and patterns of energy consumption. However, many strategies may have some problems where high-control precision may increase energy use, or low energy use in systems may decrease indoor thermal quality. This study investigates a neural network algorithm with an adaptive model on how to control the supply air conditions reflecting learned data. During the process, the adaptive model complements the signals from the network to independently maintain the comfort level within setting ranges. Although the proposed model effectively optimizes energy consumption and supply air conditions, it achieves quite improved comfort levels about 14% more efficient than comparison models. Consequently, it is confirmed that a network and learning algorithm equipped with an adaptive controller properly responds to users’ comfort levels and system’s energy consumption in a single space. The improved performance in space levels can be significant in places where many spaces are systematically connected, and in places which require a high consistency of indoor thermal comfort. Another advantage of the proposed model is that it properly reduces an increase in energy consumption despite an intensive strategy is utilized to improve thermal comfort.

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

2020 ◽  
Vol 12 (20) ◽  
pp. 8515 ◽  
Author(s):  
Jonghoon Ahn
Keyword(s):  
Energy Consumption ◽  
Thermal Comfort ◽  
Energy Use ◽  
Fuzzy Logic Controller ◽  
Sustainable Use ◽  
Adaptive Controller ◽  
Comfort Level ◽  
Effective Control ◽  
Control Precision ◽  
Neural Network Controller

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.

scholarly journals Impact of Passive Cooling on Thermal Comfort in a Single-Family Building for Current and Future Climate Conditions

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5332
Author(s):  
Krzysztof Grygierek ◽  
Izabela Sarna
Keyword(s):  
Thermal Comfort ◽  
Natural Ventilation ◽  
Energy Use ◽  
Weather Data ◽  
Passive Cooling ◽  
Climate Data ◽  
Single Family ◽  
Climate Conditions ◽  
Building Simulations ◽  
Family Building

Today, there is a great deal of emphasis on reducing energy use in buildings for both economic and environmental reasons. Investors strongly encourage the insulating of buildings. Buildings without cooling systems can lead to a deterioration in thermal comfort, even in transitional climate areas. In this article, the effectiveness of natural ventilation in a passive cooling building is analyzed. Two options are considered: cooling with external air supplied to the building by fans, or by opening windows (automatically or by residents). In both cases, fuzzy controllers for the cooling time and supply airflow control are proposed and optimized. The analysis refers to a typical Polish single-family building. Simulations are made with the use of the EnergyPlus program, and the model is validated based on indoor temperature measurement. The calculations were carried out for different climate data: standard and future (warmed) weather data. Research has shown that cooling with external air can effectively improve thermal comfort with a slight increase in heating demand. However, to be able to reach the potential of such a solution, fans should be used.

scholarly journals Thermal Perception in Mild Climate: Adaptive Thermal Models for Schools

2019 ◽  
Vol 11 (14) ◽  
pp. 3948 ◽  
Author(s):  
Miguel Ángel Campano ◽  
Samuel Domínguez-Amarillo ◽  
Jesica Fernández-Agüera ◽  
Juan José Sendra
Keyword(s):  
Thermal Comfort ◽  
Natural Ventilation ◽  
Energy Use ◽  
Thermal Environment ◽  
Thermal Sensation ◽  
Comfort Level ◽  
Thermal Perception ◽  
Accurate Design ◽  
Almost All ◽  
Autumn And Winter

A comprehensive assessment of indoor environmental conditions is performed on a representative sample of classrooms in schools across southern Spain (Mediterranean climate) to evaluate the thermal comfort level, thermal perception and preference, and the relationship with HVAC systems, with a comparison of seasons and personal clothing. Almost fifty classrooms were studied and around one thousand pool-surveys distributed among their occupants, aged 12 to 17. These measurements were performed during spring, autumn, and winter, considered the most representative periods of use for schools. A new proposed protocol has been developed for the collection and subsequent analysis of data, applying thermal comfort indicators and using the most frequent predictive models, rational (RTC) and adaptive (ATC), for comparison. Cooling is not provided in any of the rooms and natural ventilation is found in most of the spaces during midseasons. Despite the existence of a general heating service in almost all classrooms in the cold period, the use of mechanical ventilation is limited. Heating did not usually provide standard set-point temperatures. However, this did not lead to widespread complaints, as occupants perceive the thermal environment as neutral—varying greatly between users—and show a preference for slightly colder environments. Comparison of these thermal comfort votes and the thermal comfort indicators used showed a better fit of thermal preference over thermal sensation and more reliable results when using regional ATC indicators than the ASHRAE adaptive model. This highlights the significance of inhabitants’ actual thermal perception. These findings provide useful insight for a more accurate design of this type of building, as well as a suitable tool for the improvement of existing spaces, improving the conditions for both comfort and wellbeing in these spaces, as well as providing a better fit of energy use for actual comfort conditions.

scholarly journals Improving Optimization Solution for Facility Layout Problem with Thermal Comfort

2020 ◽  
Vol 76 (3) ◽  
pp. 49-61
Author(s):  
Noorhadila Mohd Bakeri ◽  
Mohd Faizal Omar ◽  
Mohd Nasrun Mohd Nawi ◽  
Faizal Baharum
Keyword(s):  
Thermal Comfort ◽  
Energy Use ◽  
Energy Savings ◽  
Facility Layout ◽  
Classical Problem ◽  
High Energy ◽  
Comfort Level ◽  
Layout Problem ◽  
Facility Layout Problem ◽  
Satisfaction Rate

Facility Layout Problem (FLP) can be considered as a classical problem in quantitative studies. However, the literature in FLP are largely neglected the thermal comfort as part of the objective function. Today, energy savings for buildings are a major concern in the world as they cover a big portion of energy use. The public room consumes high energy use because of its ability to occupy many people at one time. Issues arise when each person has a dissimilar thermal satisfaction rate, while each area in a room provides a different temperature. There are many factors that influence the people dispersion in the room including the facility layout. However, it is really testing to handle an air-conditioning control () system by considering the mention factors to ensure the thermal satisfaction is increased and energy is reduced. Since lack of report on thermal factors in Facility Layout Problem (FLP) area, this work aims to optimize the temperature setting of an system at the best point and achieving the finest plan for the facility layout in a room. Further, our ultimate goals to maximize the thermal comfort level and reduce energy consumption are able to accomplish. A non-linear mathematical model is utilized to optimize the thermal satisfaction rate () and room layout. At the end of the article, we proposed an Evolutionary Algorithm (EA) to find a quality solution or near optimal since it is hard to solve this problem in a reasonable time.

scholarly journals Impacts on Indoor Thermal Comfort and Heating Energy Use in Hellenic Dwellings from Occupant Behavioral Reactions

2021 ◽  
Vol 11 (14) ◽  
pp. 6254
Author(s):  
Elena G. Dascalaki ◽  
Constantinos A. Balaras
Keyword(s):  
Thermal Comfort ◽  
Energy Use ◽  
Energy Savings ◽  
Local Heating ◽  
Economic Recession ◽  
Heating System ◽  
Building Stock ◽  
Heating Systems ◽  
Indoor Thermal Comfort ◽  
Energy Audits

In an effort to reduce the operational cost of their dwellings, occupants may even have to sacrifice their indoor thermal comfort conditions. Following the economic recession in Greece over recent years, homeowners have been forced to adapt their practices by shortening heating hours, lowering the indoor thermostat settings, isolating spaces that are not heated or even turning off their central heating system and using alternative local heating systems. This paper presents the results from over 100 occupant surveys using questionnaires and walk-through energy audits in Hellenic households that documented how occupants operated the heating systems in their dwellings and the resulting indoor thermal comfort conditions and actual energy use. The results indicate that the perceived winter thermal comfort conditions were satisfactory in only half of the dwellings, since the actual operating space heating periods averaged only 5 h (compared with the assumed 18 h in standard conditions), while less than half heated their entire dwellings and only a fifth maintained an indoor setpoint temperature of 20 °C, corresponding to standard comfort conditions. Mainstream energy conservation measures include system maintenance, switching to more efficient systems, reducing heat losses and installing controls. This information is then used to derive empirical adaptation factors for bridging the gap between the calculated and actual energy use, making more realistic estimates of the expected energy savings following building renovations, setting prudent targets for energy efficiency and developing effective plans toward a decarbonized building stock.

Optimizing Thermal Comfort and Energy Use for Learning Environments

2021 ◽  
pp. 111181
Author(s):  
Mary Taylor ◽  
Nathan Brown ◽  
Donghyun Rim
Keyword(s):  
Thermal Comfort ◽  
Learning Environments ◽  
Energy Use

scholarly journals Determinism

2021 ◽  
Vol 20 (5) ◽  
pp. 1-34
Author(s):  
Edward A. Lee
Keyword(s):  
Quantum Physics ◽  
Deterministic Model ◽  
Physical World ◽  
Deterministic Models ◽  
Newtonian Physics ◽  
Physical Systems ◽  
Newton’S Laws ◽  
And Behavior

This article is about deterministic models, what they are, why they are useful, and what their limitations are. First, the article emphasizes that determinism is a property of models, not of physical systems. Whether a model is deterministic or not depends on how one defines the inputs and behavior of the model. To define behavior, one has to define an observer. The article compares and contrasts two classes of ways to define an observer, one based on the notion of “state” and another that more flexibly defines the observables. The notion of “state” is shown to be problematic and lead to nondeterminism that is avoided when the observables are defined differently. The article examines determinism in models of the physical world. In what may surprise many readers, it shows that Newtonian physics admits nondeterminism and that quantum physics may be interpreted as a deterministic model. Moreover, it shows that both relativity and quantum physics undermine the notion of “state” and therefore require more flexible ways of defining observables. Finally, the article reviews results showing that sufficiently rich sets of deterministic models are incomplete. Specifically, nondeterminism is inescapable in any system of models rich enough to encompass Newton’s laws.

scholarly journals Soft Computing Tool Approach for Texture Classification Using Discrete Cosine Transform

2011 ◽  
pp. 12-16
Author(s):  
Pankaj H. Chandankhede
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Learning Algorithm ◽  
Texture Classification ◽  
Fuzzy Model ◽  
Performance Comparison ◽  
Cosine Transform ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Computing Models

Texture can be considered as a repeating pattern of local variation of pixel intensities. Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing are used. A feedforward neural network is used to train the backpropagation learning algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and the remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. It is observed that the proposed neuro-fuzzy model performed better than the neural network.

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