Demand Control Research Articles

Implementation of an AI ready BACS system in Treviso school with DCV (Demand Control Ventilation)

The pandemic has highlighted the extreme importance of indoor air quality (IAQ). Especially in schools, so places that have always been critical for the spread of highly contagious viral diseases, IAQ is an increasingly necessary need. In the province of Treviso, a tailored system has been installed: there are both winter and summer air conditioning, plus AHU’s capable of guaranteeing the necessary air exchange to obtain a healthy level of the air inside the classrooms. The county also intends to replicate this experience in all schools of its territory and monitor the operation of these plants continuously. The important matter is not only to implement systems equipped with HRV (heat recovery ventilation), but also to equip them with automation and control systems capable of implementing an high automation standard according to EN 15232-1 and a high SRI or Smart Readiness Indicator defined by the EPBD III directive 844/2018. A high index ensures an implementation of a distributed intelligence system all over the plant, connected to the cloud and capable of extrapolating the physical variables, then processing them with ML (machine learning) and AI (artificial intelligence) techniques optimized for the HVAC algorithms. At the end, it is not enough to detect quantities such as EI (environmental index) but it is also necessary to process the data for the purposes of predictive maintenance and much more.

Psychological stress and job satisfaction in middle management executives: a test of job demand control support model

Psychological stress and job satisfaction in middle management executives: a test of job demand control support model

A pilot study of occupant centric control stratum ventilation based on computer vision

Indoor occupant information has an obvious influence on operating parameters of heating ventilation and air conditioning (HVAC) system, which further affects occupants’ thermal comfort and energy consumption. This pilot study proposes an occupant centric control (OCC) strategy for stratum ventilation (SV) to achieve demand control ventilation (DCV). Firstly, the computer vision sensing system and deep learning algorithm are used to detect the number of occupants in real time, and the accuracy of the number of occupants in the office environment was evaluated. Then, the occupant centric stratum ventilation control strategy is designed by the dynamic changes of cooling load. Finally, the thermal comfort and air quality of the thermal environment created by the OCC strategy were evaluated through subject experiment, and the energy consumption of the HVAC system was calculated in combination with the energy consumption simulation software. This study adjusts system setting values according to actual needs, so that the HVAC system responds to the dynamic changes of the indoor cooling load in real time, creating a comfortable and healthy indoor environment in an energy efficient manner.

Pushover analysis for upgrading of existing residential masonry building

Masonry is common used for classic building construction, but is known for its seismic vulnerability. The existing regulations in Serbia (Eurocode) demand control of the behaviour of masonry buildings exposed to seismic actions. For this it is most convenient to use, for engineering purposes, non-linear static (pushover) analysis. With the aim of increasing knowledge about the seismic behavior of different structural configurations created by upgrading an existing residential two-story masonry building, a comparative study was conducted on four different proposed models. This paper presents the most significant results of pushover analyses performed on a spatial model of the structure of the existing building that needs to be upgraded and on spatial models of the structures with an additional floor level and different positioning of vertical confining elements while evaluating their seismic capacity in compliance with the regulations. Two different lateral load distributions are assumed in computation since the appropriate lateral load profile is not always obvious. Based on various approaches, there are different identified responses.

Psychological stress and job satisfaction in middle management executives: a test of job demand control support model

Psychological stress and job satisfaction in middle management executives: a test of job demand control support model

Audit and control of reactive power of a grid-connected distributed alternator

India is a dynamically developing country in which 60% to 65% of the industry consumes electricity. Many Indian firms are improving their technology, so the demand and supply of electricity is growing. With the help of energy audits, it can reduce the gap between energy demand and supply as much as possible, which is good for all sectors and it can manage the maximum demand for energy in companies and improve the power factor by reducing energy losses and improving energy efficiency. This paper is a preliminary investigation by Vandana Global Limited, Raipur. Comprehensive load study of industries with 7000 KVA monthly contract demand, new loads have been introduced in this industry, highlighting the need for significant energy conservation measures. To evaluate the savings, a full energy audit must be carried out. After energy analysis of the monthly electricity bill, the four-month average power factor is 0.92, therefore the grid power factor can be increased as inductive loads are fewer. It can use the automatic reactive control panel to improving to the grid power factor by adjusting the required reactive power from the grid connected alternator. The system is effective due to cost savings, return on investment and profit. Time of Day (TOD) and conserve energy meter readings were used to measure demand control. Energy Audit energy efficiency solutions reduce energy waste and energy costs. In general, special attention was paid to reduce demand losses and energy costs. This paper recommends using genuine energy in the right place and organizing the use of energy through an energy audit.

Day-ahead complex power scheduling in a reconfigurable hybrid-energy islanded microgrid with responsive demand considering uncertainty and different load models

This paper proposes a new operation management scheme (OMS) for next-day complex power scheduling in an islanded reconfigurable microgrid. Energy supply in the microgrid comes from wind turbines and photovoltaic generators as renewable dispersed generators (RDG). Meanwhile, there are two microturbine units and a fuel cell unit as dispatchable distributed generators (DDG). Also, a battery bank energy storage system (BESS) is integrated. The microgrid has a number of controllable tie-switches to change the microgrid configuration and supplies residential, commercial, and industrial loads with different electrical models. Some of loads participate in a demand control program. The OMS is mathematically formulated as a multi-period mixed-integer stochastic nonlinear optimization. It has to decide the hourly power share of each DDG, the states of the control switches, and specifications of demand response. The OMS aims to minimize the total operation cost, unmet load, and curtailed renewable power maintaining all technical constraints. The uncertainty of renewable resources and load is analyzed in the OMS by Monte-Carlo simulation while scenario-reduction approach is used to reduce the computational burden. Besides, a new version of bat algorithm (IBA) is developed as a robust optimization solver. Simulation results and comparative evaluation of the OMS are presented using a 45-bus case study system to prove the merits of the method.

The Role of Executive Function in the Effectiveness of Multi-Component Interventions Targeting Physical Activity Behavior in Office Workers.

A knowledge gap remains in understanding how to improve the intervention effectiveness in office workers targeting physically active (PA) behavior. We aim to identify the modifying effect of executive function (EF) on the intervention effectiveness targeting PA-behaviors, and to verify whether the observed effect varies by Job Demand Control (JDC) categories. This workplace-based intervention study included 245 participants who were randomized into a control group and two intervention arms—promoting physical activity (iPA) group or reducing sedentary behavior (iSED) group. The interventions were conducted through counselling-based cognitive behavioral therapy and team activities over 6 months. PA-behaviors were measured by an accelerometer. EF was assessed by the Trail Making Test-B, Stroop, and n-back test. The JDC categories were measured by the demand control questionnaire. Higher EF level at baseline was significantly associated with the intervention effect on increased sleep time (β-coefficient: 3.33, p = 0.003) and decreased sedentary time (−2.76, p = 0.049) in the iSED-group. Participants with active jobs (high job demands, high control) presented significantly increased light-intensity PA in the iSED-group in comparison to the control group. Among participants with a high level of EF and active jobs, relative to the control group, the iPA-group showed a substantial increase in light-intensity PA (1.58, p = 0.036) and the iSED-group showed a tendency of reducing sedentary behavior (−5.35, p = 0.054). The findings suggest that office workers with a high EF and active jobs may benefit most from an intervention study targeting PA-behaviors.

Data Reconstruction of Wireless Sensor Network and Zonal Demand Control in a Large-Scale Indoor Space Considering Thermal Coupling

An indoor high and open space is characterized by high mobility of people and uneven temperature distribution, so the conventional design and operation of air conditioning systems makes it difficult to regulate the air conditioning system precisely and efficiently. Thus, a Wireless Sensor Network was constructed in an indoor space located in Hong Kong to monitor the indoor environmental parameters of the space and improve the temperature control effectively. To ensure the continuity of the measurement data, three algorithms for reconstructing temperature, relative humidity and carbon dioxide data were implemented and compared. The results demonstrate the accuracy of support vector regression model and multiple linear regression model is higher than Back Propagation neural network model for reconstructing temperature data. Multiple linear regression is the most convenient from the perspective of program complexity, computing speed and difficulty in obtaining input conditions. Based on the data we collected, the traditional single-input-single-output control, zonal temperature control and the proposed zonal demand control methods were modeled on a Transient System Simulation Program (TRNSYS) control platform, the thermal coupling between the subzones without physical partition was taken into account, and the mass transfer between the virtual boundaries was calculated by an external CONTAM program. The simulation results showed the proposed zonal demand control can alleviate the over-cooling or over-heating phenomenon in conventional temperature control, thermal comfort and energy reduction is enhanced as well.

A comparative study of different pull control strategies in multi-product manufacturing systems using discrete event simulation

Pull production control strategies coordinate manufacturing operations based on actual demand. Up to now, relevant publications mostly examine manufacturing systems that produce a single type of a product. In this research, we examine the CONWIP, Base Stock, and CONWIP/Kanban Hybrid pull strategies in multi-product manufacturing systems. In a multi-product manufacturing system, several types of products are manufactured by utilizing the same resources. We develop queueing network models of multi-stage, multi-product manufacturing systems operating under the three aforementioned pull control strategies. Simulation models of the alternative production systems are implemented using an open-source software. A comparative evaluation of CONWIP, Base Stock and CONWIP/Kanban Hybrid in multi-product manufacturing is carried out in a series of simulation experiments with varying demand arrival rates, setup times and control parameters. The control strategies are compared based on average wait time of backordered demand, average finished products inventories, and average length of backorders queues. The Base Stock strategy excels when the manufacturing system is subjected to high demand arrival rates. The CONWIP strategy produced consistently the highest level of finished goods inventories. The CONWIP/Kanban Hybrid strategy is significantly affected by the workload that is imposed on the system.

The Association of Work-related Stress According to the Demand-Control Model With Aggravation of Pre-existing Disease During the First State of COVID-19 Emergency in Japan.

BackgroundThe job environment has changed a lot during the period of the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to investigate the association between work-related stress and aggravation of pre-existing disease in workers during the first state of COVID-19 emergency in Japan.MethodsData were obtained from a large internet survey conducted between August 25 and September 30, 2020 in Japan. Participants who reported that they had a job as well as current history of disease(s) (ie, pre-existing conditions) were included (n = 3,090). Aggravation of pre-existing disease during the state of emergency was self-reported. Work-related stress from April 2020 (since the state of COVID-19 emergency) was assessed according to a job demand–control model. Multivariable logistic regression models were used to analyze the association.ResultsAggravation of pre-existing diseases was reported by 334 participants (11%). The numbers of participants with high demand and low control were 112 (18%) and 100 (14%), respectively. Compared to medium demand, high demand was significantly associated with aggravation of pre-existing diseases (odds ratio 1.77; 95% confidence interval, 1.30–2.42). Low control compared to medium control was also significantly associated with aggravation of pre-existing diseases (odds ratio 1.39; 95% confidence interval, 1.02–1.92).ConclusionWork-related stress during the first state of COVID-19 emergency was associated with aggravation of pre-existing disease during that period.

Experimental study of five different VAV air terminal devices under variable heat gain conditions in simulated office and meeting rooms

Demand control of the air distribution systems is crucial to the satisfactory thermal environment under variable heat gain conditions in an energy efficient manner. In this study, the airflow patterns, ventilation performance and local thermal comfort have been experimentally investigated in mock-up meeting and office rooms with a variable air volume (VAV) system. The Active Diffuser, which had a constant inlet air velocity, was compared with some commonly used diffusers: Rectangular; Swirl-Radial; Swirl-Compact and Multi-Nozzle Diffusers. In the test, there were four different heat gain levels: high heat gain (46 W/m2) in a meeting room setting; high (46 W/m2), medium (24 W/m2) and low (11 W/m2) heat gain in an office room setting, respectively. The corresponding specific supply airflow rates in the high, medium and low heat gain cases were 4.3 L/s m2, 2.1 L/s m2, 1 L/s m2, respectively. Visualized figures showed the effect of VAV on airflow patterns were quite different among different diffusers. The indexes including air velocity, ventilation effectiveness, effective draft temperature, draught rating and so on were therefore analysed. By using the Active Diffuser, the airflow distribution and most of these indexes were more uniform and more stable under the VAV. Although the Rectangular, Swirl-Compact, Swirl-Radial and Multi-Nozzle Diffusers might perform better in one or two cases regarding some indexes, their airflow patterns and performances of indexes were unstable under the VAV. Thus, the Active Diffuser was recommended for the VAV system for the application where heat gains vary significantly.

Day-level relationships between work, physical activity, and well-being: Testing the physical activity-mediated demand-control (pamDC) model

ABSTRACT The physical activity-mediated Demand-Control model (Häusser, J. A., & Mojzisch, A. (2017). The physical activity-mediated Demand–Control (pamDC) model: Linking work characteristics, leisure time physical activity, and well-being. Work & Stress, 31(3), 209–232. doi:10.1080/02678373.2017.1303759 ) posits that leisure-time physical activity (LTPA) is an important variable mediating the effects of job characteristics on well-being. Building on this model, we hypothesise that day-specific job demands and job control predict LTPA after work (via self-regulation and self-determination). LTPA after work, in turn, is predicted to be positively related to evening well-being. We tested these hypotheses by conducting a 14-day ambulatory assessment study. A sample of 207 employees wore an accelerometer to monitor physical activity and answered, three times a day, questionnaires addressing job characteristics, self-regulatory capacity, self-determination, and well-being. Results show that day-specific job demands were negatively related to LTPA and different indicators of evening well-being. We also found evidence for the mediation of the relationship between day-specific job demands and evening well-being via LTPA after work. However, contrary to our predictions, job control was not related to LTPA on a daily basis. These findings help to advance theory and, in particular, to understand the importance of LTPA for employee well-being.

FPGA-Based Cost-Effective and Resource Optimized Solution of Predictive Direct Current Control for Power Converters

Recent advances in power converter applications with highly demanding control goals require the efficient implementation of superior control strategies. However, the real-time application of such control strategies demands high computational power that necessitates efficient digital controllers like field programmable gate array (FPGA). The inherent parallelism offered by FPGAs minimizes the execution time and exhibits an excellent cost-performance trade-off. In addition, rapid advancements in FPGA technology with a broad portfolio of intellectual property (IP) cores, design tools, and robust embedded processors resulted in a design paradigm shift. This article proposes a low-cost solution for the resource-optimized implementation of dynamic, highly accurate, and computationally intensive finite state-predictive direct current control (FS-PDCC). The challenges for implementing complex control algorithms for power converters are discussed in detail, and the control is implemented in Intel’s low-cost non-volatile FPGA-MAX®10. An efficient design methodology using finite state machine (FSM) is adopted to achieve time/resource-efficient implementation. The parallel and pipelined architecture of FPGA provides better resource utilization with high execution speed. The experimental results prove the efficiency of FPGA-based cost-effective solutions that offer superior performance with better output quality.

Evaluation of the performance of demand control ventilation system for school buildings located in the hot climate of Saudi Arabia

Evaluation of the performance of demand control ventilation system for school buildings located in the hot climate of Saudi Arabia

Methodology for sizing Battery Energy Storage Systems to support the 13.8 kV distribution grid

The increased of intermittent sources in the Brazilian electrical matrix and the growing load demand bring insecurity to the electrical power system. Battery Energy Storage Systems (BESS) enables several improvements to the electrical power system according to the applications for which they were sized, such as power smoothing, voltage control, contingency performance, load leveling, and demand control. BESS correct sizing is a vital issue for an optimized operation and it is intrinsically associated with the application in which it will be used. Thus, this article aims to present a methodology for BESS dimensioning and a case study using that methodology. Two systems were sized to meet the connection requirements in a feeder of the distribution network of Companhia Energética de Minas Gerais (CEMIG).

Virtual Inertia Control Strategy of Traction Converter in High-Speed Railways Based on Feedback Linearization of Sliding Mode Observer

Aiming at the low-frequency oscillation (LFO) phenomenon in high-speed railways, this paper proposes a feedback linearized virtual inertia control (VIC) strategy of the traction converter based on a sliding mode observer (SMO) to improve the robustness of the system and suppress the occurrence of LFO. First, to optimize the total harmonic distortion of the traction grid-side current, the inner loop controller is designed as the feedback linearization control. Then, the voltage outer loop is designed as a combination of the VIC designed in this paper and SMO, which enhances the system inertia and reduces the impact of external interference on the system, while reducing the controller's demand for electrical power on the DC-side. Furthermore, a small-signal model is established to analyze the stability of the single-phase vehicle-grid system under the proposed control. The influence of the control parameters on stability is discussed in detail. Finally, the comparison of software simulation and hardware-in-the-loop experimental results between traditional PI control, feedback linearization control, feedback+SMO, feedback+VIC and the proposed control method shows that the proposed control has better dynamic and static characteristics, and can effectively inhibit the occurrence of LFO.

IEA EBC Annex 68 – Consequences on ventilation and hygrothermal operation of buildings

The objective of the IEA EBC Annex 68 Project, “Indoor Air Quality Design and Control in Low Energy Residential Buildings”, has been to develop the fundamental basis for optimal design and control strategies for good Indoor Air Quality (IAQ) in highly energy efficient residential buildings. Focus has been on emission of chemical pollutants from building products and use of ventilation to alleviate IAQ effects. The question has been whether new paradigms for demand control should be developed based on knowledge from this project.The paper gives an overview of the project’s activities with regards to: - Gathering of laboratory and field data on pollution sources in buildings. - Formulation of a so-called “similarity approach” to predict emissions of volatile organic compounds based on knowledge from moisture transfer properties. - Gathering of a set of contemporary models to simulate the combined heat, air, moisture and pollution conditions of buildings and their assemblies. Based on this background, the project has identified and described an extended set of amenable ways to optimize the provision of ventilation and air-conditioning and to assess possibilities to bring this knowledge into practice. The paper gives an overview of the suggested solutions and their conditions.

Understanding Rural Women's Domestic Work Experiences (DWE) in Ibadan, Nigeria: Development of a Measurement Tool Using Confirmatory Factor Analysis.

Gender norms prescribe domestic labor as primarily a female’s responsibility in developing countries. Many domestic tasks depend on access to water, so the physical, emotional, and time demands of domestic labor may be exacerbated for women living in water-insecure environments. We developed a set of domestic work experience (DWE) measures tailored to work in rural areas in developing countries, assessed rural Nigerian women’s DWE, and examined relationships among the measures. Interviewer-administered survey data were collected between August and September from 256 women in four rural Nigerian communities. Latent factors of DWE were identified by analyzing survey items using confirmatory factor analysis. Pearson’s correlation was used to examine relationships among latent factor scores, and multivariate linear regression models were used to determine if factor scores significantly differed across socio-demographic characteristics. The DWE measures consisted of latent factors of the physical domain (frequency of common domestic tasks, water sourcing and carriage, experience of water scarcity), the psychosocial domain (stress appraisal and demand–control), and the social domain (social support). Significant correlations were observed among the latent factors within and across domains. Results revealed the importance of measuring rural Nigerian women’s DWE using multiple and contextual approaches rather than relying solely on one exposure measure. Multiple inter-related factors contributed to women’s DWE. Water insecurity exacerbated the physical and emotional demands of domestic labor DWE varied across age categories and pregnancy status among rural Nigerian women.

“Demand Control” an Innovative Way of Reducing the HVAC System’s Energy Consumption

According to EIA, the Heating Ventilation and Air Conditioning (HAVC) systems account for about 25% of the U.S.’s total commercial building’s energy use. Therefore, advanced modeling and optimization methods of the system components and operation offer great ways to reduce energy consumption in all types of buildings and mainly commercial buildings. This research introduced an innovative integrated two-level optimization technique for the HVAC system to reduce the total energy consumption while improving the indoor thermal comfort level. The process uses actual system performance data collected for the building automation systems (BAS) to create accurate component modeling and optimization process as the first level of optimization (MLO). Artificial neural networks were chosen to be the tool used to serve the process of modeling. The second optimization level utilizes the whole system-level optimization technique (SLO) using a genetic algorithm (G.A.). The proposed two-levels optimization technique will optimize the system setpoints, the supply air temperature, duct static pressure, minimum zone air flowrates, and minimum outdoor air ventilation rate. The proposed technique has contributed to the field of modeling and optimization of HVAC systems through several new contributions. (1) Implementing the demand control methodology with the optimization process to modify the electricity consumption power profile when the demand signal is received. (2) Implement the occupancy schedule inputs into the optimization process to adjust the ventilation airflow rates accordingly. (3) Implement the real-time zone occupancy sensor readings and adjust the zone’s ventilation flowrates and minimum flowrates. (4) Lastly, implementing the method of zone minimum air flowrates setpoint rests to reduce reheat requirements. The proposed optimization process was tested and validated, resulting in savings in the total energy consumed by the chilled water VAV system by 13.4%, 22.4 %, followed by 31% for July, February, and October, respectively.