Pub Date : 2022-07-05DOI: 10.23919/SpliTech55088.2022.9854246
Qudama Al-Yasiri, M. Szabó
Applying phase change materials (PCMs) for thermal energy storage is a prosperous technology nowadays in different heat storage and temperature regulation applications. These materials proved high potential in the building sector towards a sustainable and efficient built environment. In this paper, PCM incorporated building roof and walls was investigated experimentally to investigate the indoor temperature enhancement, heat gain reduction and CO2 emission saving. Two rooms, one loaded with PCM and the other without, were built and tested in southern Iraq under severe hot weather conditions for three consecutive days. The results indicated positive thermal behaviour of PCM in which the average indoor temperature was improved by $2 {{}^{circ}mathrm{C}}$ during day hours. Moreover, the average heat gain was reduced by 54-54.69 $mathrm{W}$, and CO2 emissions were saved by 1.299-1.348 kg per day. The results indicated that the PCM could not maintain acceptable thermal comfort in the studied location, and using air-conditioning systems is required.
{"title":"Building envelope integrated phase change material under hot climate towards efficient energy and CO2 emssion saving","authors":"Qudama Al-Yasiri, M. Szabó","doi":"10.23919/SpliTech55088.2022.9854246","DOIUrl":"https://doi.org/10.23919/SpliTech55088.2022.9854246","url":null,"abstract":"Applying phase change materials (PCMs) for thermal energy storage is a prosperous technology nowadays in different heat storage and temperature regulation applications. These materials proved high potential in the building sector towards a sustainable and efficient built environment. In this paper, PCM incorporated building roof and walls was investigated experimentally to investigate the indoor temperature enhancement, heat gain reduction and CO2 emission saving. Two rooms, one loaded with PCM and the other without, were built and tested in southern Iraq under severe hot weather conditions for three consecutive days. The results indicated positive thermal behaviour of PCM in which the average indoor temperature was improved by $2 {{}^{circ}mathrm{C}}$ during day hours. Moreover, the average heat gain was reduced by 54-54.69 $mathrm{W}$, and CO2 emissions were saved by 1.299-1.348 kg per day. The results indicated that the PCM could not maintain acceptable thermal comfort in the studied location, and using air-conditioning systems is required.","PeriodicalId":295373,"journal":{"name":"2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127214276","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}
Pub Date : 2022-07-05DOI: 10.23919/SpliTech55088.2022.9854312
G. Aruta, F. Ascione, N. Bianco, R. D. de Masi, G. M. Mauro, G. Vanoli
This study applies a simulation- and optimization-based framework using artificial neural networks for the model predictive control (MPC) of space heating systems. The case study is a real low-energy building located in Benevento (South Italy). The framework is envisioned to provide optimal values of setpoint temperatures on a day-ahead planning horizon to minimize energy cost and thermal discomfort, based on weather forecasts. A Pareto multi-objective approach is applied, modeling thermal comfort via the adaptive theory of ASHRAE 55, i.e., assessing a comfort penalty function. The optimization problem is solved by running a genetic algorithm, using nonlinear autoregressive networks with exogenous inputs (NARX) as simulation tool. The nets are trained on the outputs of a validated EnergyPlus model, showing good agreement. The framework is tested addressing a typical day of the winter season and using EnergyPlus weather data to simulate weather forecasts. The proposed optimal solution presents running cost for heating of 1.1 c€/m2day and a daily comfort penalty of 15 °C h. This means a cost saving around 9% and a reduction of discomfort around 7% compared to a reference control strategy at fixed setpoint, i.e., 21°C. Besides the proposed virtual implementation, the framework can be integrated into automation systems for real-time MPC.
{"title":"Model predictive control based on genetic algorithm and neural networks to optimize heating operation of a real low-energy building","authors":"G. Aruta, F. Ascione, N. Bianco, R. D. de Masi, G. M. Mauro, G. Vanoli","doi":"10.23919/SpliTech55088.2022.9854312","DOIUrl":"https://doi.org/10.23919/SpliTech55088.2022.9854312","url":null,"abstract":"This study applies a simulation- and optimization-based framework using artificial neural networks for the model predictive control (MPC) of space heating systems. The case study is a real low-energy building located in Benevento (South Italy). The framework is envisioned to provide optimal values of setpoint temperatures on a day-ahead planning horizon to minimize energy cost and thermal discomfort, based on weather forecasts. A Pareto multi-objective approach is applied, modeling thermal comfort via the adaptive theory of ASHRAE 55, i.e., assessing a comfort penalty function. The optimization problem is solved by running a genetic algorithm, using nonlinear autoregressive networks with exogenous inputs (NARX) as simulation tool. The nets are trained on the outputs of a validated EnergyPlus model, showing good agreement. The framework is tested addressing a typical day of the winter season and using EnergyPlus weather data to simulate weather forecasts. The proposed optimal solution presents running cost for heating of 1.1 c€/m2day and a daily comfort penalty of 15 °C h. This means a cost saving around 9% and a reduction of discomfort around 7% compared to a reference control strategy at fixed setpoint, i.e., 21°C. Besides the proposed virtual implementation, the framework can be integrated into automation systems for real-time MPC.","PeriodicalId":295373,"journal":{"name":"2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)","volume":"42 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121014703","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}
Pub Date : 2022-07-05DOI: 10.23919/SpliTech55088.2022.9854374
Talal Halabi, Adel Abusitta, Glaucio H. S. Carvalho, B. Fung
With billions of devices already connected to the network's edge, the Internet of Things (IoT) is shaping the future of pervasive computing. Nonetheless, IoT applications still cannot escape the need for the computing resources available at the fog layer. This becomes challenging since the fog nodes are not necessarily secure nor reliable, which widens even further the IoT threat surface. Moreover, the security risk appetite of heterogeneous IoT applications in different domains or deploy-ment contexts should not be assessed similarly. To respond to this challenge, this paper proposes a new approach to optimize the allocation of secure and reliable fog computing resources among IoT applications with varying security risk level. First, the security and reliability levels of fog nodes are quantitatively evaluated, and a security risk assessment methodology is defined for IoT services. Then, an online, incentive-compatible mechanism is designed to allocate secure fog resources to high-risk IoT offloading requests. Compared to the offline Vickrey auction, the proposed mechanism is computationally efficient and yields an acceptable approximation of the social welfare of IoT devices, allowing to attenuate security risk within the edge network.
{"title":"Incentivized Security-Aware Computation Offloading for Large-Scale Internet of Things Applications","authors":"Talal Halabi, Adel Abusitta, Glaucio H. S. Carvalho, B. Fung","doi":"10.23919/SpliTech55088.2022.9854374","DOIUrl":"https://doi.org/10.23919/SpliTech55088.2022.9854374","url":null,"abstract":"With billions of devices already connected to the network's edge, the Internet of Things (IoT) is shaping the future of pervasive computing. Nonetheless, IoT applications still cannot escape the need for the computing resources available at the fog layer. This becomes challenging since the fog nodes are not necessarily secure nor reliable, which widens even further the IoT threat surface. Moreover, the security risk appetite of heterogeneous IoT applications in different domains or deploy-ment contexts should not be assessed similarly. To respond to this challenge, this paper proposes a new approach to optimize the allocation of secure and reliable fog computing resources among IoT applications with varying security risk level. First, the security and reliability levels of fog nodes are quantitatively evaluated, and a security risk assessment methodology is defined for IoT services. Then, an online, incentive-compatible mechanism is designed to allocate secure fog resources to high-risk IoT offloading requests. Compared to the offline Vickrey auction, the proposed mechanism is computationally efficient and yields an acceptable approximation of the social welfare of IoT devices, allowing to attenuate security risk within the edge network.","PeriodicalId":295373,"journal":{"name":"2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123290851","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}
Pub Date : 2022-07-05DOI: 10.23919/SpliTech55088.2022.9854379
Aitor Almeida, Aritz Bilbao-Jayo, Liss Hernández, Laura Lopez-Perez, Estefanía Estùvez-Priego, G. Fico, K. Taylor, S. Singer, F. Mercalli, D. E. Filippidou, Elena Martinelli, S. Cavalieri, L. Licitra
As survivorship chances for cancer improve, the necessity to properly manage the quality of life post-treatment increases. Head and Neck Cancer is one of the most prevalent ones (being the seventh most common cancer in the world). In this paper we introduce the BD4QoL Ontology, which provides a comprehensive and integrated data model for HN cancer survivors. The presented ontology models several relevant areas of the knowledge domain: the patients clinical and demographic data, the questionnaires commonly used to ascertain their QoL and the related behavioral and emotional traits that can be used to infer the QoL.
{"title":"An Ontology for Quality of Life Modeling in Head and Neck Cancer","authors":"Aitor Almeida, Aritz Bilbao-Jayo, Liss Hernández, Laura Lopez-Perez, Estefanía Estùvez-Priego, G. Fico, K. Taylor, S. Singer, F. Mercalli, D. E. Filippidou, Elena Martinelli, S. Cavalieri, L. Licitra","doi":"10.23919/SpliTech55088.2022.9854379","DOIUrl":"https://doi.org/10.23919/SpliTech55088.2022.9854379","url":null,"abstract":"As survivorship chances for cancer improve, the necessity to properly manage the quality of life post-treatment increases. Head and Neck Cancer is one of the most prevalent ones (being the seventh most common cancer in the world). In this paper we introduce the BD4QoL Ontology, which provides a comprehensive and integrated data model for HN cancer survivors. The presented ontology models several relevant areas of the knowledge domain: the patients clinical and demographic data, the questionnaires commonly used to ascertain their QoL and the related behavioral and emotional traits that can be used to infer the QoL.","PeriodicalId":295373,"journal":{"name":"2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123793756","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}
Pub Date : 2022-07-05DOI: 10.23919/SpliTech55088.2022.9854308
Hadi El Hajj Chehade, B. Uguen, S. Collardey
This paper is based on an experimental setup for RFID tag characterization using an Alien 9900+ reader. The first part presents the measurement setup and proposes an RSSI (Received Signal Strength Indicator) to received power formula for the alien reader as well as different RFID power profiles. In the second part, we introduce a new profile named power IC effective sensitivity profile and we propose several performance indicators. Those profiles and indicators are illustrated on a set of 9 differents tags and shown to provide a rich information about the tag design and read range performances.
{"title":"Transmission and Receiving Power Profiles for RFID Tags Perfomances Evaluation","authors":"Hadi El Hajj Chehade, B. Uguen, S. Collardey","doi":"10.23919/SpliTech55088.2022.9854308","DOIUrl":"https://doi.org/10.23919/SpliTech55088.2022.9854308","url":null,"abstract":"This paper is based on an experimental setup for RFID tag characterization using an Alien 9900+ reader. The first part presents the measurement setup and proposes an RSSI (Received Signal Strength Indicator) to received power formula for the alien reader as well as different RFID power profiles. In the second part, we introduce a new profile named power IC effective sensitivity profile and we propose several performance indicators. Those profiles and indicators are illustrated on a set of 9 differents tags and shown to provide a rich information about the tag design and read range performances.","PeriodicalId":295373,"journal":{"name":"2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131752993","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}
Pub Date : 2022-07-05DOI: 10.23919/SpliTech55088.2022.9854288
Waldemar Titov, T. Schlegel
The User Acceptance in Autonomous Vehicle is the main subject that has received the attention of researcher and professional in all around the world. The main objective of the paper is to encourage people towards technological acceptance of Autonomous vehicles. All benefit, after technological difficulties, do not come without a certain amount of challenges. The present challenges of Autonomous Vehicle are Assurance of system and Software, Sensing and Connectivity, Judgment, and Verification and Validation. This paper helps to provide more information regarding this technology. It helps the users to understand how safe and best this technology to use. To better predict, explain and increase User acceptance, we need to better understand why people will not accept or reject this technology. To use the knowledge obtained in order to foresee improved knowledge has on the actions of both the automotive industry and government. These papers provide practical evidence of road accidents as well as it compares Manual and Automatic Cars, which helps users to decide what good for them. There are questions asked by every individual who has to be answered. Among those questions, there is a common question asked by every user “How safe are you in an autonomous vehicle”?
{"title":"Promoting User Acceptance in Autonomous Driving","authors":"Waldemar Titov, T. Schlegel","doi":"10.23919/SpliTech55088.2022.9854288","DOIUrl":"https://doi.org/10.23919/SpliTech55088.2022.9854288","url":null,"abstract":"The User Acceptance in Autonomous Vehicle is the main subject that has received the attention of researcher and professional in all around the world. The main objective of the paper is to encourage people towards technological acceptance of Autonomous vehicles. All benefit, after technological difficulties, do not come without a certain amount of challenges. The present challenges of Autonomous Vehicle are Assurance of system and Software, Sensing and Connectivity, Judgment, and Verification and Validation. This paper helps to provide more information regarding this technology. It helps the users to understand how safe and best this technology to use. To better predict, explain and increase User acceptance, we need to better understand why people will not accept or reject this technology. To use the knowledge obtained in order to foresee improved knowledge has on the actions of both the automotive industry and government. These papers provide practical evidence of road accidents as well as it compares Manual and Automatic Cars, which helps users to decide what good for them. There are questions asked by every individual who has to be answered. Among those questions, there is a common question asked by every user “How safe are you in an autonomous vehicle”?","PeriodicalId":295373,"journal":{"name":"2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116565847","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}
Pub Date : 2022-07-05DOI: 10.23919/SpliTech55088.2022.9854276
Terezija Matijašević, T. Antić, T. Capuder
With greater integration of smart meters, an opportunity to increase the observability of the traditionally unobservable low-voltage distribution networks is created. The purpose of such meters is mainly to collect and store data on end-user consumption for billing purposes, and it is these large data flows that open up a wide range of analyses to Distribution System Operators. Leading in this is the prediction of end-user consumption, which finds its application especially in determining network losses for more efficient planning and operation of distribution networks. Due to the complicated features of the collected load series data, the application of synthetic curves for the consumption forecasting problem is abandoned and energy utilities are turning to more complex solutions, most often based on machine learning algorithms. Therefore, this paper presents a machine learning-based model for forecasting losses in a low-voltage distribution network. Power flow simulation tools are frequently used to estimate and predict active power losses but are applied only in the case of available network topology and elements data. Hence, in this paper, special emphasis is placed on a model that does not rely on network data, but only on historical measurements collected from smart meters. The model is tested on a real-world distribution network with more than 150 end-users. The results show the effectiveness of the model in forecasting active power losses of the observed network, but also highlight the sensitivity of the model to errors, which is a good basis for the implementation of additional algorithms and variables as a means to enabling near real-time operation planning of distribution networks.
{"title":"Machine learning-based forecast of secondary distribution network losses calculated from the smart meters data","authors":"Terezija Matijašević, T. Antić, T. Capuder","doi":"10.23919/SpliTech55088.2022.9854276","DOIUrl":"https://doi.org/10.23919/SpliTech55088.2022.9854276","url":null,"abstract":"With greater integration of smart meters, an opportunity to increase the observability of the traditionally unobservable low-voltage distribution networks is created. The purpose of such meters is mainly to collect and store data on end-user consumption for billing purposes, and it is these large data flows that open up a wide range of analyses to Distribution System Operators. Leading in this is the prediction of end-user consumption, which finds its application especially in determining network losses for more efficient planning and operation of distribution networks. Due to the complicated features of the collected load series data, the application of synthetic curves for the consumption forecasting problem is abandoned and energy utilities are turning to more complex solutions, most often based on machine learning algorithms. Therefore, this paper presents a machine learning-based model for forecasting losses in a low-voltage distribution network. Power flow simulation tools are frequently used to estimate and predict active power losses but are applied only in the case of available network topology and elements data. Hence, in this paper, special emphasis is placed on a model that does not rely on network data, but only on historical measurements collected from smart meters. The model is tested on a real-world distribution network with more than 150 end-users. The results show the effectiveness of the model in forecasting active power losses of the observed network, but also highlight the sensitivity of the model to errors, which is a good basis for the implementation of additional algorithms and variables as a means to enabling near real-time operation planning of distribution networks.","PeriodicalId":295373,"journal":{"name":"2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116492315","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}
Pub Date : 2022-07-05DOI: 10.23919/SpliTech55088.2022.9854304
M. Barachi, Sinan Salman, S. Mathew
With the current focus on sustainability and food safety, we are witnessing increasing demand for food freshness monitoring and traceability. This requirement is of critical importance for perishable food that has a short shelf-life and is easily impacted by environmental conditions such as temperature and humidity. Several approaches have been proposed in the literature for the monitoring of perishable food. Typically relying on RFIDs, chemical, and microbiological sensors, those approaches aim at giving an indication about the freshness level of various perishable food items and alert when a carton has spoiled. Such approaches can be costly due to their requirement of sophisticated settings and equipment, in addition to focusing on a coarse-grained classification of whether a carton has perished or not. In this work, we propose an affordable sensor-embedded carton that is able to accurately detect the spoilage of even a single item of food in real-time, to enable timely intervention and prevent contamination of the rest of the items. To design this smart carton, we relied on gas diffusion simulations that informed the optimal placement and number of sensors required. Furthermore, a warehouse architecture encompassing smart cartons, smart pallets, and an analytics server is proposed as a large-scale food monitoring approach. Such an approach opens the door for accurate and real-time monitoring of perishable food facilitates inventory management and minimizes food safety and waste concerns.
{"title":"A Sensor-Embedded Smart Carton for the Real-Time Monitoring of Perishable Foods' Lifetime","authors":"M. Barachi, Sinan Salman, S. Mathew","doi":"10.23919/SpliTech55088.2022.9854304","DOIUrl":"https://doi.org/10.23919/SpliTech55088.2022.9854304","url":null,"abstract":"With the current focus on sustainability and food safety, we are witnessing increasing demand for food freshness monitoring and traceability. This requirement is of critical importance for perishable food that has a short shelf-life and is easily impacted by environmental conditions such as temperature and humidity. Several approaches have been proposed in the literature for the monitoring of perishable food. Typically relying on RFIDs, chemical, and microbiological sensors, those approaches aim at giving an indication about the freshness level of various perishable food items and alert when a carton has spoiled. Such approaches can be costly due to their requirement of sophisticated settings and equipment, in addition to focusing on a coarse-grained classification of whether a carton has perished or not. In this work, we propose an affordable sensor-embedded carton that is able to accurately detect the spoilage of even a single item of food in real-time, to enable timely intervention and prevent contamination of the rest of the items. To design this smart carton, we relied on gas diffusion simulations that informed the optimal placement and number of sensors required. Furthermore, a warehouse architecture encompassing smart cartons, smart pallets, and an analytics server is proposed as a large-scale food monitoring approach. Such an approach opens the door for accurate and real-time monitoring of perishable food facilitates inventory management and minimizes food safety and waste concerns.","PeriodicalId":295373,"journal":{"name":"2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114989921","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}
Pub Date : 2022-07-05DOI: 10.23919/SpliTech55088.2022.9854322
D. Foito, V. Pires, A. Cordeiro, T. Amaral, M. Chaves, A. Pires, J. Martins
This paper focuses on a proposal for a system based on a photovoltaic (PV) supply for a powered water pumping. The system consists in a switched reluctance machine (SRM) controlled by a multilevel converter and fed by PV panels associated to a DC/DC converter. The multilevel power converter proposed to control the SRM was designed to minimize the switches and to support the balance of the two input capacitors. The DC/DC converter consists in a hybrid solution that merges a Buck-Boost converter with a Sepic converter. They use a topology solution in which the input current presents a reduced ripple and only requires one switch. This DC/DC converter is also characterized by a dual output to adapt to the multilevel converter. The control system and a maximum power point tracking (MPPT) algorithm are also presented. The operation of this system will be verified by tests that are done by computer simulations.
{"title":"A SRM for a PV Powered Water Pumping System Based on a Multilevel Converter and DC/DC Dual Output Converter","authors":"D. Foito, V. Pires, A. Cordeiro, T. Amaral, M. Chaves, A. Pires, J. Martins","doi":"10.23919/SpliTech55088.2022.9854322","DOIUrl":"https://doi.org/10.23919/SpliTech55088.2022.9854322","url":null,"abstract":"This paper focuses on a proposal for a system based on a photovoltaic (PV) supply for a powered water pumping. The system consists in a switched reluctance machine (SRM) controlled by a multilevel converter and fed by PV panels associated to a DC/DC converter. The multilevel power converter proposed to control the SRM was designed to minimize the switches and to support the balance of the two input capacitors. The DC/DC converter consists in a hybrid solution that merges a Buck-Boost converter with a Sepic converter. They use a topology solution in which the input current presents a reduced ripple and only requires one switch. This DC/DC converter is also characterized by a dual output to adapt to the multilevel converter. The control system and a maximum power point tracking (MPPT) algorithm are also presented. The operation of this system will be verified by tests that are done by computer simulations.","PeriodicalId":295373,"journal":{"name":"2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129933513","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}
Pub Date : 2022-07-05DOI: 10.23919/SpliTech55088.2022.9854270
Anton Kerčov, Tamara Bajc, Milan Gojak, Maja Todorović, N. Pivac, S. Nižetić
The paper deals with an analysis of existing thermal comfort models based on exergy approach and the impact of models' input parameters to the calculation results. The aim of this paper is to present the results of the application of five different thermal comfort models based on the exergy analysis, to compare them, and to determine if they coincide with the results obtained by using Fanger's model. While models that are the most commonly used to evaluate and predict thermal comfort conditions are based on the first law of thermodynamics, a handful of authors used both the first and the second law of thermodynamics in order to develop new thermal comfort models. Even though the optimal comfort conditions according to these models may not differ by large margin from the optimal thermal comfort conditions according to Fanger's model, it is concluded that justification of using models based on the exergy analysis to evaluate thermal comfort is very dependent on the input parameters, which should all be taken into consideration separately,
{"title":"Comparison between different thermal comfort models based on the exergy analysis","authors":"Anton Kerčov, Tamara Bajc, Milan Gojak, Maja Todorović, N. Pivac, S. Nižetić","doi":"10.23919/SpliTech55088.2022.9854270","DOIUrl":"https://doi.org/10.23919/SpliTech55088.2022.9854270","url":null,"abstract":"The paper deals with an analysis of existing thermal comfort models based on exergy approach and the impact of models' input parameters to the calculation results. The aim of this paper is to present the results of the application of five different thermal comfort models based on the exergy analysis, to compare them, and to determine if they coincide with the results obtained by using Fanger's model. While models that are the most commonly used to evaluate and predict thermal comfort conditions are based on the first law of thermodynamics, a handful of authors used both the first and the second law of thermodynamics in order to develop new thermal comfort models. Even though the optimal comfort conditions according to these models may not differ by large margin from the optimal thermal comfort conditions according to Fanger's model, it is concluded that justification of using models based on the exergy analysis to evaluate thermal comfort is very dependent on the input parameters, which should all be taken into consideration separately,","PeriodicalId":295373,"journal":{"name":"2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132688202","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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