Cities are accelerating policies to electrify their energy sectors as a key strategy for reducing greenhouse gas emissions. In densely populated cities with cold climates, the building sector often accounts for over 70% of total energy consumption during winter seasons. In such cold climate megacities, the common practice for heating building spaces involves burning oil or gas. A major shift from this conventional approach towards electric-based heating technologies could have far-reaching implications. In this work, we focus on New York City (NYC), where buildings account for over 75% of total energy consumption used during winter seasons. The city has adopted policies aimed at achieving deep decarbonization by targeting buildings as a primary source of emissions. We evaluate the potential energy infrastructure and environmental impacts of such major shifts by focusing on the adoption of air source heat pumps from natural gas boiler. The Weather Research and Forecasting model, coupled with a multi-layer building environment parameterization and building energy model is used to perform this analysis. A city-scale case study is performed over the winter month of January 2021. Simulation results show good agreement with surface weather stations. We show that a shift of heating systems from gas to electricity results in an equivalent peak energy demand from 21,500 MW to 5.800 MW, while reducing the peak UHI by 2.5-3°C. Results highlight potential tradeoffs in adaptation strategies for cities, which may be necessary in the context of increasing decarbonization policies.
{"title":"On the Electrification of Winter Season in Cold Climate Megacities-The Case of New York City","authors":"Harold Gamarro, Jorge Gonzalez-Cruz","doi":"10.1115/1.4063377","DOIUrl":"https://doi.org/10.1115/1.4063377","url":null,"abstract":"Cities are accelerating policies to electrify their energy sectors as a key strategy for reducing greenhouse gas emissions. In densely populated cities with cold climates, the building sector often accounts for over 70% of total energy consumption during winter seasons. In such cold climate megacities, the common practice for heating building spaces involves burning oil or gas. A major shift from this conventional approach towards electric-based heating technologies could have far-reaching implications. In this work, we focus on New York City (NYC), where buildings account for over 75% of total energy consumption used during winter seasons. The city has adopted policies aimed at achieving deep decarbonization by targeting buildings as a primary source of emissions. We evaluate the potential energy infrastructure and environmental impacts of such major shifts by focusing on the adoption of air source heat pumps from natural gas boiler. The Weather Research and Forecasting model, coupled with a multi-layer building environment parameterization and building energy model is used to perform this analysis. A city-scale case study is performed over the winter month of January 2021. Simulation results show good agreement with surface weather stations. We show that a shift of heating systems from gas to electricity results in an equivalent peak energy demand from 21,500 MW to 5.800 MW, while reducing the peak UHI by 2.5-3°C. Results highlight potential tradeoffs in adaptation strategies for cities, which may be necessary in the context of increasing decarbonization policies.","PeriodicalId":326594,"journal":{"name":"ASME Journal of Engineering for Sustainable Buildings and Cities","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123015293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the pharmaceutical industry (PMI), the major portion of energy is consumed in heat ventilation air conditioning (HVAC) system, therefore building energy management systems (BEMS) primarily focus on optimizing the energy consumption in HVAC systems. The two operation modes of HVAC, function mode (FM) and non-function mode (NFM), is descriptively explained with their role in improving the flexibility of demanded energy. Both modes are also exposed with hybrid optimization of multiple electric renewables (HOMER) software analysis from an economic perspective. Concerning net present cost (NPC) and cost of energy (COE) constraints, the FM/NFM of HVAC is preferable to the FM. This paper recognizes a comparative evaluation of several demand response (DR) alliances to deliver a comprehensive image of the suitability of DR alliances for different PMIs. Further, the paper also explores an innovative concept in the form of a control algorithm and discussion the relevant challenges and future opportunities. Moreover, the use of renewable energy systems (RESs) for enhancing energy management (EM) flexibility with the economy in the PMI or other industries is emphasized through DR alliances. This review study could be helpful to the PMI in terms of managing energy demand and also incorporating DR as an essential aspect of EM.
{"title":"Utilization of distinct HVAC operation modes to improve demand response flexibility in the pharmaceutical industry and economic analysis for optimization by HOMER software","authors":"Ankush Gupta, Sathans Suhag","doi":"10.1115/1.4063249","DOIUrl":"https://doi.org/10.1115/1.4063249","url":null,"abstract":"\u0000 In the pharmaceutical industry (PMI), the major portion of energy is consumed in heat ventilation air conditioning (HVAC) system, therefore building energy management systems (BEMS) primarily focus on optimizing the energy consumption in HVAC systems. The two operation modes of HVAC, function mode (FM) and non-function mode (NFM), is descriptively explained with their role in improving the flexibility of demanded energy. Both modes are also exposed with hybrid optimization of multiple electric renewables (HOMER) software analysis from an economic perspective. Concerning net present cost (NPC) and cost of energy (COE) constraints, the FM/NFM of HVAC is preferable to the FM. This paper recognizes a comparative evaluation of several demand response (DR) alliances to deliver a comprehensive image of the suitability of DR alliances for different PMIs. Further, the paper also explores an innovative concept in the form of a control algorithm and discussion the relevant challenges and future opportunities. Moreover, the use of renewable energy systems (RESs) for enhancing energy management (EM) flexibility with the economy in the PMI or other industries is emphasized through DR alliances. This review study could be helpful to the PMI in terms of managing energy demand and also incorporating DR as an essential aspect of EM.","PeriodicalId":326594,"journal":{"name":"ASME Journal of Engineering for Sustainable Buildings and Cities","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121777657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mahdi Houchati, F. Alabtah, AbdMonem Beitelmal, M. Khraisheh
The utilization of solar energy as a source of renewable energy has been a subject of interest for researchers in recent years. Despite recent advances in promoting solar energy, its intermittent and unpredictable nature limits its widespread utilization in manufacturing facilities. This research paper focuses on the utilization of solar energy for efficient scheduling of manufacturing processes and minimizing building HVAC energy requirements while mainteaining thermal comfort conditions for the workers. The work proposes an energy-aware dynamic scheduling procedure to minimize production and building costs by optimizing the utilization of an onsite Photovoltaic (PV) system energy generation. The proposed method takes into account various factors such as the availability of solar energy, energy consumption of different manufacturing processes, and thermal requirements of the building. A stochastic energy prediction algorithm is developed to forecast the hourly one-day-ahead solar resources, based on year-long solar radiation observations collected from an outdoor solar test facility in Qatar. This study shows that using the forecasted PV output improves the overall efficiency of manufacturing processes and building HVAC energy requirements, thus achieving up to a 20% reduction in energy costs. These findings help the development of sustainable manufacturing systems and decrease the negative environmental impacts from industries.
{"title":"Towards Sustainable Manufacturing Facilities: Utilization of Solar Energy for Efficient Scheduling of Manufacturing Processes","authors":"Mahdi Houchati, F. Alabtah, AbdMonem Beitelmal, M. Khraisheh","doi":"10.1115/1.4063212","DOIUrl":"https://doi.org/10.1115/1.4063212","url":null,"abstract":"\u0000 The utilization of solar energy as a source of renewable energy has been a subject of interest for researchers in recent years. Despite recent advances in promoting solar energy, its intermittent and unpredictable nature limits its widespread utilization in manufacturing facilities. This research paper focuses on the utilization of solar energy for efficient scheduling of manufacturing processes and minimizing building HVAC energy requirements while mainteaining thermal comfort conditions for the workers. The work proposes an energy-aware dynamic scheduling procedure to minimize production and building costs by optimizing the utilization of an onsite Photovoltaic (PV) system energy generation. The proposed method takes into account various factors such as the availability of solar energy, energy consumption of different manufacturing processes, and thermal requirements of the building. A stochastic energy prediction algorithm is developed to forecast the hourly one-day-ahead solar resources, based on year-long solar radiation observations collected from an outdoor solar test facility in Qatar. This study shows that using the forecasted PV output improves the overall efficiency of manufacturing processes and building HVAC energy requirements, thus achieving up to a 20% reduction in energy costs. These findings help the development of sustainable manufacturing systems and decrease the negative environmental impacts from industries.","PeriodicalId":326594,"journal":{"name":"ASME Journal of Engineering for Sustainable Buildings and Cities","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129610874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Conventional physics-based building energy models (BEMs) consider all of the building characteristics in order to accurately simulate their energy usage, requiring an extensive, complex, and costly process, particularly for existing buildings. The purpose of this work is to present a methodology for predicting the energy consumption of buildings using deep neural networks (NNs). Three machine learning algorithms including a linear regression model, a multi-layer perceptron (MLP) NN, and a convolutional NN (CNN) model are proposed to solve an energy consumption regression problem using outside dry bulb temperature as the only input. To assess these methods, a building in Melbourne, FL is considered and modeled in EnergyPlus. Ten years of data were used as inputs to the EnergyPlus model, and the energy consumption was calculated accordingly. The input to the machine learning algorithm (average daily dry bulb temperature) and the output (daily total energy consumption) are used for training. Cross-validation was performed on the trained model using actual weather data measured on-site at the building location. The results showed that all three proposed machine learning algorithms were trained successfully and were able to solve the regression problem with high accuracy. However, the CNN model provided the best results. This work also investigates different data filtering techniques that provide the best positive correlation between inputs and outputs. The presented framework provides a readily simple model that allows accurate prediction of outputs when supplied with new inputs and can be used by a wide range of end users.
{"title":"ENERGY FORECASTING IN BUILDINGS USING DEEP NEURAL NETWORKS","authors":"Mariana Migliori, H. Najafi","doi":"10.1115/1.4063213","DOIUrl":"https://doi.org/10.1115/1.4063213","url":null,"abstract":"\u0000 Conventional physics-based building energy models (BEMs) consider all of the building characteristics in order to accurately simulate their energy usage, requiring an extensive, complex, and costly process, particularly for existing buildings. The purpose of this work is to present a methodology for predicting the energy consumption of buildings using deep neural networks (NNs). Three machine learning algorithms including a linear regression model, a multi-layer perceptron (MLP) NN, and a convolutional NN (CNN) model are proposed to solve an energy consumption regression problem using outside dry bulb temperature as the only input. To assess these methods, a building in Melbourne, FL is considered and modeled in EnergyPlus. Ten years of data were used as inputs to the EnergyPlus model, and the energy consumption was calculated accordingly. The input to the machine learning algorithm (average daily dry bulb temperature) and the output (daily total energy consumption) are used for training. Cross-validation was performed on the trained model using actual weather data measured on-site at the building location. The results showed that all three proposed machine learning algorithms were trained successfully and were able to solve the regression problem with high accuracy. However, the CNN model provided the best results. This work also investigates different data filtering techniques that provide the best positive correlation between inputs and outputs. The presented framework provides a readily simple model that allows accurate prediction of outputs when supplied with new inputs and can be used by a wide range of end users.","PeriodicalId":326594,"journal":{"name":"ASME Journal of Engineering for Sustainable Buildings and Cities","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131224719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
HVAC systems are notorious for their high energy consumption in buildings, particularly in regions with extreme cooling or heating demands. Air filters play a vital role in these systems, affecting both energy efficiency and indoor air quality. However, high-efficiency filters, due to their significant increase in airflow resistance, require excessive energy compared to low-efficiency filters. This poses a challenge in finding the optimal compromise between reducing energy consumption and enhancing indoor air quality. To address this challenge, a meticulous selection process is crucial in achieving a middle ground that satisfies both objectives. Proper sizing and design of air filters are therefore essential for successful HVAC projects. This paper introduces the utilization of optimization techniques as decision-support tools to determine the optimal design parameters of commonly used HVAC air filters under various scenarios. The developed model incorporates multiple objectives and design criteria, including life-cycle cost (LCC), filter size, and efficiency. By leveraging the Differential Evolution (DE) optimization technique, an algorithm is developed to forecast a range of optimal solutions (Pareto front) based on predefined system criteria and boundary conditions. The model is extensively tested and demonstrates exceptional performance in returning optimal solutions, in addition to the capability of narrowing down and converging to a single value. This methodology holds significant potential in assisting investment decisions concerning HVAC air filters, providing valuable insights for optimizing energy efficiency while ensuring satisfactory indoor air quality.
{"title":"Parametric Analysis and Multi-Objective Optimization for Energy-Efficient and High-Performance HVAC Air filter Design and Selection","authors":"Mohammed Al-Azba, M. Mahgoub","doi":"10.1115/1.4063052","DOIUrl":"https://doi.org/10.1115/1.4063052","url":null,"abstract":"\u0000 HVAC systems are notorious for their high energy consumption in buildings, particularly in regions with extreme cooling or heating demands. Air filters play a vital role in these systems, affecting both energy efficiency and indoor air quality. However, high-efficiency filters, due to their significant increase in airflow resistance, require excessive energy compared to low-efficiency filters. This poses a challenge in finding the optimal compromise between reducing energy consumption and enhancing indoor air quality. To address this challenge, a meticulous selection process is crucial in achieving a middle ground that satisfies both objectives. Proper sizing and design of air filters are therefore essential for successful HVAC projects. This paper introduces the utilization of optimization techniques as decision-support tools to determine the optimal design parameters of commonly used HVAC air filters under various scenarios. The developed model incorporates multiple objectives and design criteria, including life-cycle cost (LCC), filter size, and efficiency. By leveraging the Differential Evolution (DE) optimization technique, an algorithm is developed to forecast a range of optimal solutions (Pareto front) based on predefined system criteria and boundary conditions. The model is extensively tested and demonstrates exceptional performance in returning optimal solutions, in addition to the capability of narrowing down and converging to a single value. This methodology holds significant potential in assisting investment decisions concerning HVAC air filters, providing valuable insights for optimizing energy efficiency while ensuring satisfactory indoor air quality.","PeriodicalId":326594,"journal":{"name":"ASME Journal of Engineering for Sustainable Buildings and Cities","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131307714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this research work, optimization of heat exchange between borehole heat exchanger (BHE) and the ground soil for space cooling and heating applications, incorporating the optimum thermal effectiveness of BHE has been reported. Initially, Taguchi technique is employed to optimize the effectiveness of borehole heat exchanger. Later, the experimental data of 24 hours are coupled with the theoretically optimized parameters to compute the optimum heat exchange during peak summer and peak winter seasons. In the Taguchi optimization approach, six control variables at three levels are employed and a standard, L27 (36) orthogonal array is selected for the analysis. Among the six control variables, thermal conductivity of the grouting material is observed to be the most influential parameter and tube radius of BHE as the least parameter in the optimized thermal effectiveness of the BHE. Both the experiments for space heating and cooling were conducted on a 17.5 kW cooling capacity ground source heat pump system (GSHP), connected with five parallelly connected double U-tube BHE and one single U-tube BHE. To compute the optimum heat transfer to/ from the BHE, time dependent borehole temperature was incorporated to include the dynamic thermal load of the GSHP system. After incorporating the Taguchi optimized thermal effectiveness in the experimental data, there is an enhancement of 30% to 48% of heat rejection into the ground during summer season, whereas in winter season there is an enhancement of 35% to 52% of heat extraction from the ground.
{"title":"Optimization of heat interaction between borehole heat exchanger and ground using Taguchi method during space cooling and heating operation of GSHP system","authors":"Shylendra Kumar, K. Murugesan","doi":"10.1115/1.4063051","DOIUrl":"https://doi.org/10.1115/1.4063051","url":null,"abstract":"\u0000 In this research work, optimization of heat exchange between borehole heat exchanger (BHE) and the ground soil for space cooling and heating applications, incorporating the optimum thermal effectiveness of BHE has been reported. Initially, Taguchi technique is employed to optimize the effectiveness of borehole heat exchanger. Later, the experimental data of 24 hours are coupled with the theoretically optimized parameters to compute the optimum heat exchange during peak summer and peak winter seasons. In the Taguchi optimization approach, six control variables at three levels are employed and a standard, L27 (36) orthogonal array is selected for the analysis. Among the six control variables, thermal conductivity of the grouting material is observed to be the most influential parameter and tube radius of BHE as the least parameter in the optimized thermal effectiveness of the BHE. Both the experiments for space heating and cooling were conducted on a 17.5 kW cooling capacity ground source heat pump system (GSHP), connected with five parallelly connected double U-tube BHE and one single U-tube BHE. To compute the optimum heat transfer to/ from the BHE, time dependent borehole temperature was incorporated to include the dynamic thermal load of the GSHP system. After incorporating the Taguchi optimized thermal effectiveness in the experimental data, there is an enhancement of 30% to 48% of heat rejection into the ground during summer season, whereas in winter season there is an enhancement of 35% to 52% of heat extraction from the ground.","PeriodicalId":326594,"journal":{"name":"ASME Journal of Engineering for Sustainable Buildings and Cities","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134222539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study evaluates for the first time the suitability of typical meteorological year (TMY) weather data for simulating the performance of buildings that are entirely conditioned by ambient energy. A home in Durango, CO was simulated with TMY data, with real data for 1998-2020 and with extreme meteorological year (XMY) data. For this climate, indoor temperature in a house designed with TMY data drops below the range of comfortable indoor temperature (20°C – 25°C) for 16 of 23 years, including as low as 13°C during 2008. With the thermal time constant of the house adjusted for each data set to maintain comfort, the required time constants for the real data ranged from 1.178 to 7.56 days with mean of 3.14 and median of 2.38, while the TMY value was 1.862 for a percentile rank of 0.318. XMY data did not produce significantly better results. Correlation of the time constant to weather parameters showed that the maximum interval during which 24-hour average solar load ratio remains below 1 is a promising index for identifying the most challenging year. Until more representative TMY and XMY weightings are developed for ambient-conditioned buildings across other climates, it is advisable that current TMY data be used only for preliminary design and multi-year simulations be conducted for final design.
{"title":"A methodology to assess the suitability of typical meteorological year weather data for simulating the performance of buildings conditioned entirely by ambient energy","authors":"M. Sharp","doi":"10.1115/1.4063053","DOIUrl":"https://doi.org/10.1115/1.4063053","url":null,"abstract":"\u0000 This study evaluates for the first time the suitability of typical meteorological year (TMY) weather data for simulating the performance of buildings that are entirely conditioned by ambient energy. A home in Durango, CO was simulated with TMY data, with real data for 1998-2020 and with extreme meteorological year (XMY) data. For this climate, indoor temperature in a house designed with TMY data drops below the range of comfortable indoor temperature (20°C – 25°C) for 16 of 23 years, including as low as 13°C during 2008. With the thermal time constant of the house adjusted for each data set to maintain comfort, the required time constants for the real data ranged from 1.178 to 7.56 days with mean of 3.14 and median of 2.38, while the TMY value was 1.862 for a percentile rank of 0.318. XMY data did not produce significantly better results. Correlation of the time constant to weather parameters showed that the maximum interval during which 24-hour average solar load ratio remains below 1 is a promising index for identifying the most challenging year. Until more representative TMY and XMY weightings are developed for ambient-conditioned buildings across other climates, it is advisable that current TMY data be used only for preliminary design and multi-year simulations be conducted for final design.","PeriodicalId":326594,"journal":{"name":"ASME Journal of Engineering for Sustainable Buildings and Cities","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127272147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Characterized by high temperatures, strong solar radiation, and prolonged sunshine hours. Air-conditioning (AC) is a necessary component for comfortable living, accounting for up to 70% of residential electricity load. With high consumption, abundant solar resources, and the country's commitment to sustainable solutions, rooftop photovoltaics (PV) represent a promising option for mitigating energy consumption in Qatar. This paper investigates the use of solar ACs in Qatar's extreme climate conditions, developing a standalone solar air-conditioning system simulation model using local historical weather data from the Qatar Environment and Energy Research Institute (QEERI). Three optimization energy management strategies were created to minimize or eliminate the need for costly battery energy storage, a concern due to high costs and hazards in extreme heat. The tested strategies showed the potential to reduce battery storage capacity by up to 15% by managing heat inertia. Complete elimination of battery storage was feasible, although it resulted in some end-of-day indoor comfort drop, which could be mitigated through cooling storage. This paper highlights the potential of solar air conditioning in Qatar, demonstrating the efficacy of the proposed optimizations and providing valuable insights for further research and implementation of solar AC systems in the region.
{"title":"Solar Air-conditioning Case Studies for Qatar Climate Conditions","authors":"Mohammed Al-Azba, Zhaohui Cen, A. Abotaleb","doi":"10.1115/1.4062840","DOIUrl":"https://doi.org/10.1115/1.4062840","url":null,"abstract":"\u0000 Characterized by high temperatures, strong solar radiation, and prolonged sunshine hours. Air-conditioning (AC) is a necessary component for comfortable living, accounting for up to 70% of residential electricity load. With high consumption, abundant solar resources, and the country's commitment to sustainable solutions, rooftop photovoltaics (PV) represent a promising option for mitigating energy consumption in Qatar. This paper investigates the use of solar ACs in Qatar's extreme climate conditions, developing a standalone solar air-conditioning system simulation model using local historical weather data from the Qatar Environment and Energy Research Institute (QEERI). Three optimization energy management strategies were created to minimize or eliminate the need for costly battery energy storage, a concern due to high costs and hazards in extreme heat. The tested strategies showed the potential to reduce battery storage capacity by up to 15% by managing heat inertia. Complete elimination of battery storage was feasible, although it resulted in some end-of-day indoor comfort drop, which could be mitigated through cooling storage. This paper highlights the potential of solar air conditioning in Qatar, demonstrating the efficacy of the proposed optimizations and providing valuable insights for further research and implementation of solar AC systems in the region.","PeriodicalId":326594,"journal":{"name":"ASME Journal of Engineering for Sustainable Buildings and Cities","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128834111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using a one-dimensional model for transient heat conduction through building enclosure walls, the present research examines the effects of thermo-physical building envelope parameters on transient heat exchange, peak cooling, and heating load for northern part of India. For space cooling and heating applications, the thermal performance of four distinct walling systems commonly employed in the climatic conditions of India was examined. Results demonstrate that when the thermal conductivity of the wall increases, the time lag reduces. As wall thickness rises from 230 mm to 310 mm, there is an increase in the time lag during cooling and heating modes. Additionally, the results show that the time lag between conduction and solar load increases as wall thickness increases. As wall thermal mass increased by 20% in cooling mode, the time of peak load was shifted by 2 hours. When operating in cooling mode in contrast to heating mode, high thermal mass is more effective in shifting the time of occurrence of peak energy consumption.
{"title":"Time lag characteristics of building envelop materials on peak energy demand in typical hot and humid climate of India","authors":"Shammy Kumar, K. Murugesan, E. Rajasekar","doi":"10.1115/1.4062510","DOIUrl":"https://doi.org/10.1115/1.4062510","url":null,"abstract":"\u0000 Using a one-dimensional model for transient heat conduction through building enclosure walls, the present research examines the effects of thermo-physical building envelope parameters on transient heat exchange, peak cooling, and heating load for northern part of India. For space cooling and heating applications, the thermal performance of four distinct walling systems commonly employed in the climatic conditions of India was examined. Results demonstrate that when the thermal conductivity of the wall increases, the time lag reduces. As wall thickness rises from 230 mm to 310 mm, there is an increase in the time lag during cooling and heating modes. Additionally, the results show that the time lag between conduction and solar load increases as wall thickness increases. As wall thermal mass increased by 20% in cooling mode, the time of peak load was shifted by 2 hours. When operating in cooling mode in contrast to heating mode, high thermal mass is more effective in shifting the time of occurrence of peak energy consumption.","PeriodicalId":326594,"journal":{"name":"ASME Journal of Engineering for Sustainable Buildings and Cities","volume":"422 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115856023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The limitations of traditional construction methods can be addressed by 3D printing, a technology that prints structural buildings in layers, which reduces labor, construction time, wastage of material, and the overall cost of the structure. This paper presents a literature review of the state-of-the-art of construction using 3D printing technology. We present a definition and a brief history of 3D printing in construction and discuss research contributions. Subsequently, we describe methods of pre-printing design, 3D design programs for construction, and on-site printing methods. Furthermore, the nature of the materials used, the printing properties, and the different construction mixtures are discussed. Additionally, the effects of commonly used chemical admixtures on the properties of the concrete mix are reviewed. Moreover, mixture tests for ensuring the requirements are met and the challenges faced in the standards and regulations during printing are discussed. Subsequently, we consider successful real-world cases from various companies and controlled laboratory environments using 3D printing based on the printing method, materials used, and challenges faced by each company. Lastly, we present future recommendations to improve the capability and printing quality of 3D printing technology.
{"title":"A Critical Review of Construction Using 3D Printing Technology","authors":"Ahmed Hunbus, B. AlMangour","doi":"10.1115/1.4062730","DOIUrl":"https://doi.org/10.1115/1.4062730","url":null,"abstract":"\u0000 The limitations of traditional construction methods can be addressed by 3D printing, a technology that prints structural buildings in layers, which reduces labor, construction time, wastage of material, and the overall cost of the structure. This paper presents a literature review of the state-of-the-art of construction using 3D printing technology. We present a definition and a brief history of 3D printing in construction and discuss research contributions. Subsequently, we describe methods of pre-printing design, 3D design programs for construction, and on-site printing methods. Furthermore, the nature of the materials used, the printing properties, and the different construction mixtures are discussed. Additionally, the effects of commonly used chemical admixtures on the properties of the concrete mix are reviewed. Moreover, mixture tests for ensuring the requirements are met and the challenges faced in the standards and regulations during printing are discussed. Subsequently, we consider successful real-world cases from various companies and controlled laboratory environments using 3D printing based on the printing method, materials used, and challenges faced by each company. Lastly, we present future recommendations to improve the capability and printing quality of 3D printing technology.","PeriodicalId":326594,"journal":{"name":"ASME Journal of Engineering for Sustainable Buildings and Cities","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114096982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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