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Nowadays, there is a growing trend of electricity consumers/users installing solar panels and storage batteries in their premises to alleviate rising energy costs. Despite this, ineffective energy management undermines the potential benefits, especially when there are multiple variables and constraints under consideration. To help solve this issue, we introduce in this paper an inclusive mathematical model for devices energy consumption scheduling based on user preferences under Time-of-Use (ToU) pricing, which also combines intelligently multiple inputs (utility, renewable and storage energy). Our approach attempts to distinguish itself by using all these inputs to optimize various outcomes such as cost, Peak-to-Average Ratio (PAR), and user comfort. It also allows consumers to tune their acceptable comfort level, providing them the autonomy and freedom to choose their preferences. We formulate and solve our mathematical model, subsequently simulating it in Matlab. Validation results underscore its efficacy, showing a notable 18.57% decrease in electricity bills through the combination of renewable energy and storage systems. Furthermore, implementing the model across three operational modes with ToU pricing demonstrates a very significant bill reduction of up to 41.57%. Ultimately, our approach, particularly when coupled with ToU pricing (contrasted with flat-rate), provides users with enhanced decision-making capabilities, driving amplified cost savings and comfort levels. [ABSTRACT FROM AUTHOR]

Purpose. This study examines development strategies to improve the comfort of use in the Regional Public Library of Lima Puluh Kota Regency. Library interior design is important in creating an environment that supports learning, and research, and enhances the visitor experience. Methodology. This research uses a qualitative descriptive method. Data was collected through observation, interviews, and documentation. Results and discussion. The development strategy for enhancing user comfort at the Lima Puluh Kota District Public Library includes optimizing interior design, such as functional layouts, comfortable materials, balanced lighting, stable air temperature, soothing wall colors, and flexible spaces to support learning and research activities. With a modern and user-friendly design approach, the library becomes not only a conducive learning space but also an inclusive social interaction hub. Conclusions. The importance of interior design in creating an environment that meets user needs enhances the library's appeal as a center for learning. The library can further improve its services through modern interior design, the use of digital technology, facilities that promote comfort, community-based activities, and creative spaces to establish an inclusive and interactive library.

In recent years, the incorporation of sensing technology into residential buildings has given rise to the concept of "smart buildings", aimed at enhancing resident comfort. These buildings are typically part of interconnected neighborhoods sharing common energy sources, which makes the energy consumption a critical consideration in decision-making processes. Consequently, optimizing energy usage in smart buildings has posed significant challenges for both enterprises and governments, prompting numerous studies to address this issue. One such challenge is organizing energy usage within neighborhood networks while ensuring the user comfort and without exceeding the total energy capacity. In this paper, we present a novel mechanism that predicts the future behavior of each house based on its historical consumption data, generating a weekly schedule annotated with hourly energy usage levels (high, normal, or low) tailored to individual user needs. Additionally, we introduce an incentive-based program that rewards users with bill discounts for adhering to high energy consumption periods. The scheduling process involves extracting features from data and utilizing a genetic algorithm for construction, coupled with dynamic programming to enhance efficiency by storing house features and schedules. This enables rapid provision of suitable schedules for similar houses. Evaluation results demonstrate that the proposed technique achieves an accuracy of 92 % and improves the execution time of the optimization algorithm by 26 % . [ABSTRACT FROM AUTHOR]

With technological advancements, domestic appliances are leveraging smart technologies for getting smarter through learning from their past usage to enhance user comfort and energy efficiency. Among these, ceiling fans, though widely used in Lower- and Middle-Income Countries (LMICs) in temperate regions, still lack a cohesive system integrating all necessary sensors with a machine learning-based system to optimize their operation for comfort and energy saving and to experimentally verify the performance under different usage scenarios that could transform a high-power-consuming device into an energy-efficient system. Therefore, the present research proposes an experimentally verified and energy-efficient Artificial Intelligence of Things (AIoT)-based system that could be retrofitted with regular DC ceiling fans. An Internet of Things (IoTs) circuit, equipped with an ESP8266 microcontroller, temperature, humidity, and motion sensors, was designed to communicate with a developed Android application and an online dashboard. A total of 123 ceiling fans with the designed IoTs circuit were deployed at various household locations for two years, with manual operations for the first year. In the next year, an auto mode based on the predictions of the machine learning model was introduced. The experimental outcomes showed that the fan with added smart features reduced the energy loss by almost 50% as compared to conventional AC ceiling fans. Consequently, the carbon footprint of the appliances is reduced significantly. A high user-rated acceptability of the system, examined through a standard measure, was also achieved. [ABSTRACT FROM AUTHOR]

Under the unique environment of high humidity and high salinity of nuclear power plants, the environment should be improved to ensure staff comfort and equipment safe operation. Meanwhile, because of energy shortage, it is of great significance for building to save energy and reduce consumption. This paper first introduces the concept of Majorization to ensure user comfort with control smoothness, improving the existing two-timescale heating, ventilation, and air conditioning (HVAC) optimization model. Then, considering energy consumption, this paper formulates a multi-objective programming (MOP) problem with the objective to seek a balance between user comfort and energy consumption. The MOP model is solved by constructing the trade-off curve, where a subobjective with Majorization is also a MOP problem nested, which can be solved more simply using the characteristic of two-timescale HVAC optimization. The numerical results demonstrate the Pareto optimal solutions and the better energy-saving effect compared with the existing two-timescale model. [ABSTRACT FROM AUTHOR]

Efficient car parking management systems that minimize environmental impacts while maximizing user comfort are highly demanding for a future sustainable society. Using electric or gasoline vehicle-type information, emerging computation and communication technologies open the opportunity to provide practical solutions to achieve such goals. This paper proposes an eco-friendly smart parking management system that optimally allocates the incoming vehicles to reduce overall emissions in closed parking facilities while providing comfort incentives to the users of electric vehicles (EVs). Specifically, upon arrival of a car, the most suitable parking spot is determined by minimizing an adaptive objective function that indirectly reflects anticipatory operation for the overall performance maximization of the parking facility using electric or gasoline vehicle-type information. The adaptive objective function includes a trade-off factor that tunes driving and walking distances, relating emissions and comfort to treat incoming vehicles appropriately. The proposed system is simulated for managing a model car parking facility in a shopping complex in Japan, and the aspects related to fuel consumption, CO2 emissions, and user comfort are evaluated and benchmarked with other standard parking management systems. The proposed system reduces CO2 emissions and fuel consumption and improves parking efficiency compared to the current parking management systems, while also prioritizing user comfort. [ABSTRACT FROM AUTHOR]

The driving style of an automated vehicle (AV) needs to be comfortable to encourage the broad acceptance and use of this newly emerging transport mode. However, current research provides limited knowledge about what influences comfort, how this concept is described, and how it is measured. This knowledge is especially lacking when comfort is linked to the AV’s driving styles. This paper presents results from an online workshop with nine experts, all with hands-on experience of AVs and a long track record of research in this context. Using online tools, experts were invited to introduce concepts they considered relevant to comfort/discomfort in currently available modes of transport which offer a ride (taxi/bus/train) to users and compare these to the concepts used to define comfort and discomfort in AVs. Results showed that a wide range of terms were used to describe user comfort and discomfort for both modes. Although all terms used for existing vehicles were found to apply to AVs, additional terms were proposed for determining comfort/discomfort of AVs. For example, to enhance comfort in AVs, designers should consider good communication channels, as well as ensuring that the AV’s capabilities match users’ expectations. Results also revealed that more terms were used, overall, to define discomfort, and that a comfortable ride in AVs is not just about mitigating discomfort. New concepts specific to AVs were also revealed when considering what increases their discomfort, such as whether riders’ safety and privacy are affected, or if they feel in control. Experts’ input from the workshop was used to enhance and expand a simple conceptual framework, explaining how AV driving styles, as well as other, non-driving-related factors, affect user comfort. It is hoped that this framework provides a more comprehensive list of the concepts affecting user comfort, also allowing more accurate measurement of the concept. As well as allowing for a more accurate comparison between empirical studies measuring comfort in AVs, this study will facilitate the design of more comfortable and acceptable automated driving for future vehicles.

This study delves into the intricate relationship between human sitting posture (HSP), elbow height seated (EHS), eye level seated (ELS), and standing posture (SP), focusing on their pivotal role in ergonomic design. The research addresses a pressing issue in Nigeria, where imported products often disregard end-users' anthropometric measurements, leading to complaints and musculoskeletal disorders. The study meticulously analyzes the vital measurements of HSP, EHS and ELS, integral components in estimating SP. Through statistical analyses and regression modeling, data from 380 young adults in Nigeria are examined, considering diverse factors such as age, gender, and regional demographics. The precision and variability of these measurements, influenced by factors like race and ethnicity, are meticulously explored, emphasizing the need for tailored design solutions. This research underscores the imperative of incorporating anthropometric data, especially HSP, EHS and ELS, in product design. The findings reveal the profound impact on healthcare, industrial design, and societal inclusivity. Integrating these measurements into design processes will fostered collaboration between designers and healthcare professionals with enhancing public awareness. This study is a cornerstone in reshaping ergonomic design paradigms; ensuring products are universally accessible and beneficial, heralding a future of inclusive and comfortable living. [ABSTRACT FROM AUTHOR]

Glass façades are being increasingly applied in the typology of office buildings. The environmental quality parameters of indoor spaces have an exceptional influence on the wellbeing and productivity of users. In the past, climate defined the architecture of a particular area. Façades of contemporary office buildings have similar characteristics regardless of their location. This study comprises four office buildings with different types of glass façades located in areas of continental and coastal climate in Croatia. Also, the research relates to a graduate study course, during which the students designed a survey to gain insight into the satisfaction of users of the analyzed buildings. The aim of this research is to explore the impact of the design of glass façades in a certain climate context on users. This paper will address the following research questions: (1) Is a fully glazed office building pleasant to work in? (2) Is a fully glazed façade an appropriate solution for office buildings in hot and sunny climates? The research results show that to achieve a comfortable indoor environment for buildings' users, it is necessary to design the façade according to the orientation, especially in terms of glazing characteristics, a sun protection system, and window control. [ABSTRACT FROM AUTHOR]

Selection of a single design to delight customers may not be always possible due to the anthropometric differences in humans, wherein a hybrid design can benefit. Using adjustability, we demonstrate our approach for developing a novel forklift backrest to accommodate drivers with a wide range of body sizes. Field and laboratory evaluations were conducted to assess and improve the design. Our results indicated that the new design could provide improved comfort for longer durations. This study reveals the possibilities for human factors professionals to consider adjustability in vehicle operator compartment interiors, especially backrests and seating, of similar industrial vehicles. [ABSTRACT FROM AUTHOR]

In response to the problem that the factors affecting room temperature and its influence on load modeling and the problems of user comfort and energy consumption expenditure when rigidly bundling residential integrated energy system (RIES) and users are not fully considered in the energy management of RIES, the RIES energy management optimization model and its solution method are proposed in the paper for the participation of cooling and heating loads in tiered subsidy and electric loads in the tariff-type integrated demand response. Firstly, the discrete building heat balance equation is obtained by comprehensively considering the factors affecting the room temperature, and the mathematical model of the flexible but not fixed cold, heat and electricity loads of the building is established. Secondly, a tiered subsidy for cold and heat load participation and a tariff-type integrated demand response mechanism for electric load participation should be established. Then, taking into account of RIES's cost of purchase energy from the outside, cost of affording integrated demand response (IDR), and profit of selling energy to the users, an optimization model for optimizing energy management is established that maximizes RIES's net profit with consideration a series of operation constraints of the components and the system. The Cplex is used to solve the linearized model. Finally, a simulation example shows that the residential integrated energy system optimization strategy that takes into account the comprehensive demand response can coordinate the resources on both sides of supply and demand, thus improving the economic benefits of the system and users.

Managing energy consumption in smart buildings has become an increasingly important challenge. Efficient energy management can have a positive impact on both micro and macroeconomics. Moreover, it is essential to ensure that the comfort of smart building residents is maintained at an acceptable level. Optimization algorithms can be used to achieve user convenience while minimizing energy consumption. In this study, we propose an optimization approach that utilizes an agent-based architecture. This architecture comprises intelligent agents that communicate with each other via message exchange in a network structure consisting of three layers: (1) The switch layer monitors user preferences and comfort levels. (2) The coordination layer includes a coordinating agent that determines the optimal timing for electrical appliances to minimize electricity consumption costs and maximize user comfort. (3) The execution layer contains performer agents. Our focus in this research is on the coordination layer with the aim of reducing energy consumption costs and peak average rates, while increasing user comfort to the highest possible level. However, this optimization problem is highly complex due to the large number of electrical devices and their capabilities. To address this, we propose a hybrid method based on genetic and bat algorithms. We evaluated its performance based on objective functions and compared it with recent research on SmartHome and CU-Bems datasets. Our results demonstrate an improvement in performance

This paper proposes a home energy management system able to achieve optimized load scheduling for the operation of appliances within a given household. The system, based on the genetic algorithm, provides recommendations for the user to improve the way the energy needs of the home are handled. These recommendations not only take into account the dynamic pricing of electricity, but also the optimization for solar energy usage as well as user comfort. Historical data regarding the times at which the appliances have been used is leveraged through a statistical method to integrate the user's preference into the algorithm. Based on real life appliance consumption data collected from a household in Morocco, three scenarios are established to assess the performance of the proposed system with each scenario having different parameters. Running the scenarios on the developed MATLAB script shows a cost saving of up to 63.48% as compared to a base scenario for a specific day. These results demonstrate that significant cost saving can be achieved while maintaining user comfort. The addition of supplementary shiftable loads (i.e., an electric vehicle) to the household as well as the limitations of such home energy management systems are discussed. The main contribution of this paper is the real data and including the user comfort as a metric in in the home energy management scheme. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Glass façades are being increasingly applied in the typology of office buildings. The environmental quality parameters of indoor spaces have an exceptional influence on the wellbeing and productivity of users. In the past, climate defined the architecture of a particular area. Façades of contemporary office buildings have similar characteristics regardless of their location. This study comprises four office buildings with different types of glass façades located in areas of continental and coastal climate in Croatia. Also, the research relates to a graduate study course, during which the students designed a survey to gain insight into the satisfaction of users of the analyzed buildings. The aim of this research is to explore the impact of the design of glass façades in a certain climate context on users. This paper will address the following research questions: (1) Is a fully glazed office building pleasant to work in? (2) Is a fully glazed façade an appropriate solution for office buildings in hot and sunny climates? The research results show that to achieve a comfortable indoor environment for buildings’ users, it is necessary to design the façade according to the orientation, especially in terms of glazing characteristics, a sun protection system, and window control.

This research focuses on spatial design systems for the user's physical, environmental, social, and cultural comfort in traditional Iran houses. In this context, the spatial organization of traditional houses for the user's long-term comfort can be viewed as a model for contemporary house design. Sustainable building design solutions used in traditional Iranian architecture could be used in modern buildings. The spatial organization of traditional houses in Tabriz, Iran's cold-arid region, will be examined in this study using examples of user comfort, climate characteristics, natural energy, and material usage methods. In terms of user comfort, this study assessed the spatial, and socio-cultural characteristics of traditional houses in Tabriz. These characteristics, as well as changes over time, have resulted in a different approach in new generation residential architecture. The aim of study is to identify the effective factors in traditional residential architecture and space organization in Tabriz. As a result, physical, climatic, environmental, social, and cultural features in Tabriz's traditional residential architecture can be considered effective factors in the design for user comfort. In this sense, traditional housing architecture's space organization and user needs can be evaluated as a sustainable housing model in line with today's technology needs in contemporary housing design. [ABSTRACT FROM AUTHOR]

Smart lighting has become a universal smart product solution, with global revenues of up to US $\$ $ 5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) lighting, sensors, control, analytics, and intelligence. The Internet of things (IoT) concept with the end device, platform, and application layer plays an essential role in optimizing the advantages of LED lighting in the emergence of smart lighting. The ultimate aim of smart lighting research is to introduce low energy efficiency and high user comfort, where the latter is still in the infancy stage. This paper presents a systematic literature review (SLR) from a bird’s eye view covering full-length research topics on smart lighting, including issues, implementation targets, technological solutions, and prospects. In addition to that, this paper also provides a detailed and extensive overview of emerging machine learning techniques as a key solution to overcome complex problems in smart lighting. A comprehensive review of improving user comfort is also presented, such as the methodology and taxonomy of activity recognition as a promising solution and user comfort metrics, including light utilization ratio, unmet comfort ratio, light to comfort ratio, power reduction rate, flickering perception, Kruithof’s comfort curve, correlated color temperature, and relative mean square error. Finally, we discuss in-depth open issues and future challenges in increasing user comfort in smart lighting using activity recognition.

The smart grid has revolutionized the conventional electricity grid with the proposition of demand-side management (DSM). A DSM program enables the user to schedule its energy consumption in compliance with any pricing signal. This scheduling helps the grid operator to reduce the peak load demand and jointly benefits the user to reduce its electricity costs. Despite that, while doing so, it jeopardizes the user’s comfort. In the present paper, the authors have investigated the impact of communal DSM programs on the consumption patterns of users, including single as well as multiple households. The objective is to simultaneously minimize the electricity costs and user discomfort to make a win-win situation for both the grid operator and the user. Therefore, a multi-objective optimization problem (MOOP) has been formed to simultaneously minimize the daily electricity cost, peak to average ratio (PAR) of load demand, user discomfort, environmental emission, and total net present cost (TNPC). In order to evaluate the best scheduling method, sizing scenarios for a residential microgrid in a Southern Pakistani metropolis surrounded by rural areas are presented in this paper. The originality of this article comes from a comparison of the techno-economic and environmental performance of several sizing options for a residential load powered by renewable energy. The artificial bee colony (ABC) algorithm has been selected to solve the MOOP. The DSM programs are based upon different pricing signals, including real-time electricity pricing (RTEP), critical peak pricing (CPP), time of use (TOU), and day-ahead pricing (DAP) pricing. The results of the proposed ABC algorithm are compared with GA and standard algorithms, and they reveal the effectiveness of the proposed method. When demand response is used, the suggested optimization technique shows that the SH spring with PV/WG/grid-connected microgrid is the most investable-reliable sizing option with a minimal TNPC of $\$ $ 1405.18 for DAP tariff with SH spring. Additionally, with a reduction in emissions of 6699 kg/yr, DAP tariff with SH spring shows that PV/WG/DG/grid-connected system has the greatest impact on the environment. For DAP tariff with SH spring, optimal sizes of PV, WG and converter are 26.7 kW, 30 kW and 6.67 kW, respectively.

Copyright of Journal of Design Architecture & Engineering (FBU-DAE) / Tasarım, Mimarlık ve Mühendislik Dergisi is the property of Journal of Design Architecture & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accelerated development of demand response service provision by the residential sector is crucial for reducing carbon-emissions in the power sector. Along with the infrastructure advancement, encouraging the end users to participate is crucial. End users highly value their privacy and control, and want to be included in the service design and decision-making process when creating the daily appliance operation schedules. Furthermore, unless they are financially or environmentally motivated, they are generally not prepared to sacrifice their comfort to help balance the power system. In this paper, we present an inverse-reinforcement-learning-based model that helps create the end users' daily appliance schedules without asking them to explicitly state their needs and wishes. By using their past consumption data, the end consumers will implicitly participate in the creation of those decisions and will thus be motivated to continue participating in the provision of demand response services. • Scheduling household appliances for demand response at the residential level. • Unclear formulation of benefits and discomforts guiding demand response provision. • Applying inverse reinforcement learning to acquire rewards guiding end-user behavior. • Learning the underlying reward sufficiently to adapt to various households. • Enhancing accuracy necessitates employing sophisticated models free from assumptions. [ABSTRACT FROM AUTHOR]

Hydrogen, being a clean energy source, presents novel possibilities for the efficient exploitation of building energy. Nevertheless, the unpredictability in the production of renewable energy, the fluctuating demand from users, and the varying comfort needs within buildings provide substantial obstacles to the functioning of building energy systems. This work suggests an optimal technique for managing a comprehensive energy system based on hydrogen. The strategy takes into account the variability in energy sources and the need for user satisfaction. Firstly, a model of building integrated energy system (BIES) is created, which includes devices for utilizing hydrogen due to its adaptable qualities. Afterwards, the adaptable attributes of electrical, cooling, and heating loads within the building are carefully examined, resulting in the creation of a integrated demand response (IDR) model and a user-integrated comfort rating model. Moreover, a ladder-type carbon tax framework is implemented, employing robust optimization methods to tackle uncertainty in the production of renewable energy and demand. Finally, a model for optimizing the scheduling of energy system operation is formulated. A case analysis is performed utilizing a spacious office building located in the northern region as an instructive example. The findings indicate that the suggested approaches successfully control user involvement in demand response, decrease carbon emissions in the system, improve user energy comfort, and accomplish the economic, low-carbon, and comfortable functioning of BIES. • The quantitative evaluation assesses the effects of HOFC, LTCT, and IDR on the functioning of BIES. • Study the effects of participation in IDR on both user comfort and system operation efficiency. • Evaluating how changes in robust coefficients affect the operational efficiency of the system and user comfort. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic significantly increased the usage of various types of face masks. In addition, it triggered the rapid manufacture of new production lines of masks to cope with the unprecedented demand to overcome worldwide shortages. Such masks, which were previously used mostly by the health care personnel, became a daily necessity to the greater mainstream population. This rapid and sudden increase in their usage and the fact that new masks' innovations are progressively emerging to meet the growing global shortage requires an ongoing analysis on the factors associated with the fit and comfort while using these masks. Methods: This paper presents the first translation and validation of the R-COMFI questionnaire to evaluate the comfort of a newly developed filtering face-piece respirator by the research team at the University of Antwerp. The questionnaire, which consists of 3 sections: Discomfort, General wearing experience, and Function, was translated from English to Arabic and involved 43 participants in the Lebanese Red Cross healthcare field based in Lebanon. Results: The results showed discomfort factors that are mostly related to breathability and sweating caused by mask usage. Additionally, the results revealed that female respondents found the mask significantly less comfortable than male respondents (p-value with the two-tailed test is 0.0319), which confirmed that future validations should consider the concerns of both genders, and validated the R-COMFI translation exercise detailed in this paper. Discussion: The contribution of this paper can be pinned down into three findings. The first finding is related to the discomfort issues. The second finding highlighted a significant difference in comfort experience between females and males. The last finding is the translation validation of the R-COMFI instrument, which confirmed that the questionnaire can be applied among wider geographical locations. [ABSTRACT FROM AUTHOR]

Energy efficiency topics have been covered by several energy management approaches in the literature, including participation in demand response programs where the consumers provide load reduction upon request or price signals. In such approaches, it is very important to know in advance the electricity consumption for the future to adequately perform the energy management. In the present paper, a load forecasting service designed for office buildings is implemented. In the building, using several available sensors, different learning parameters and structures are tested for artificial neural networks and the K-nearest neighbor algorithm. Deep focus is given to the individual period errors. In the case study, the forecasting of one week of electricity consumption is tested. It has been concluded that it is impossible to identify a single combination of learning parameters as different parts of the day have different consumption patterns.

In this study, we investigate the operation of an optimal home energy management system (HEMS) with integrated renewable energy system (RES) and energy storage system (ESS) supporting electricity selling functions. A multi-objective mixed integer nonlinear programming model, including RES, ESS, home appliances and the main grid, is proposed to optimize different and conflicting objectives which are energy cost, user comfort and PAR. The effect of different selling prices on the objectives is also considered in detail. We further develop a formula for the lower bound of energy cost to help residents or engineers quickly choose best parameters of RES and ESS for their homes during the installation process. The performance of our system is verified through extensive simulations under three different scenarios of normal, economic, and smart with different selling prices using real data, and simulation results are compared in terms of daily energy cost, PAR, user's convenience and consecutive waiting time to use appliances. Numerical results clearly show that the economic scenario achieves 51.6% reduction of daily energy cost compared to the normal scenario while sacrificing the user's convenience, PAR, and consecutive waiting time by 49%, 132%, and 1 hour, respectively. On the other hand, the smart scenario shows only slight degradation of user's convenience and PAR by 2% and 18%, respectively while achieving 46.4% reduction of daily energy cost and the same level of consecutive waiting time. Furthermore, our simulation results show that a decrease of selling prices has tiny impacts on PAR and user comfort even though the daily energy cost increases.

This paper proposes an optimization approach to minimize the power consumption of an office building considering user comfort and key performance indicators. The present approach implements the power reduction by reducing power consumption of lights and air conditioners as reducible loads, and implements load shifting by considering dishwasher as shift able load. Two key performance indicators and several comfort parameters are defined to limit the power reduction of lights and air conditioners to consider the user comfort. In the context of load shifting, the complete operation cycle of dishwasher should be shifted without interruption based on the priority weight of the periods and available power. In the present optimization approach, sufficient number of periods have been considered for running the optimization, considering operational constraints and multiple levels for key performance indicators to evaluate their effectiveness. The case study of the work contains six different scenarios to validate different characteristics of the approach. Real consumption data of 20 lights, 10 air conditioners and 1 dishwasher in 20 working days have been applied to implement six different scenarios to propose a real comparable view of present approach.

In this paper, i am developing a unique optimization based real time inland load management algorithm that takes into account load ambiguity in order to minimize the energy payment for each residential user, as well as reduce the peak to average ratio to overcome the drawbacks in the stability of electrical grid. By categorizing the all residential load in different classes, i.e. must run, interruptible and uninterruptible appliances, i used the real time pricing scheme for load management. However, real time pricing creates the peak profiles when the energy demand is too high, that’s why i used the combination of real time pricing and inclining blocks rates model to improve the grid stability by reducing the peak to average ratio. A simulation results show that the proposed algorithm efficiently and effectively reduced the overall residential energy cost as well as peak to average ratio of our model for data provided.

Designing machines without involving the human factor can cause users discomfort, fatigue, and health problems. Ergonomic criteria when designing machines can increase user effectiveness. PT. Noang Prima Utama uses an extruder to produce pasta under the “LocaPasta” brand. Complaints of pain in the neck and back for 1-3 days due to standing position, bending down, and arms reaching forward for a long-time during production. The research objective was to evaluate the extruder design using the anthropometric method. Anthropometric data were tested for uniformity, data adequacy, and the determination of percentiles. Machine dimensions were compared with user anthropometer data for design improvement. When feeding materials into the hopper, the operator’s working posture needed to be more ergonomically adequate due to raised arms and pushing the material bit by bit (repetitively). The evaluation of the machine design based on anthropometric dimensions reveals several improvements. The redesigned extruder machine includes changes such as adjusting the footstep height to 27.7 cm so that the height of the setting box to 120.95 cm from the footstep (adjusting to standing shoulder height), allowing rotation of the setting box and the hopper height from the footstep is parallel to the standing elbow height which is 92.1 cm. With the implementation of the new prototype, the operator’s working posture no longer involves bending over while adjusting the machine, and the arms can comfortably rest on the hopper while feeding the material.

Despite broad adoption of Digital Human Models (DHMs) in other disciplines, the use of ergonomics in interior design heavily depends on design standards that offer rigid guidelines that do not adapt to design or human schemata. Digital ergonomic assessments and DHMs help designers predict and evaluate comfort for more human-centered designs. This paper proposes a framework to incorporate digital ergonomic evaluations to quantify and assess interior spaces' ergonomic comfort using customized DHMs. The framework demonstrates a closed feedback loop connecting 3D models with Human Builder to perform quantitative ergonomic tests while offering real-time results with design recommendations. This implementation showed that individual physical capabilities affected the users' comfort and design outcomes. [ABSTRACT FROM AUTHOR]