Pub Date : 2021-04-22DOI: 10.1109/SusTech51236.2021.9467471
Maniell Workman, D. Z. Chen, S. Musa
Photovoltaic semiconductors are diodes which produce a current when exposed to light. The ideality factor is a parameter which tells how closely a semiconductor behaves to an ideal diode. In an ideal diode, the only mechanism for hole electron recombination is direct bimolecular recombination. Because there are multiple mechanisms of recombination, there are no real devices with a perfect ideality factor. The types of recombination occurring within a device can be inferred by its ideality factor. In this work, we examine the ideality factor of perovskite solar cells to identify possible recombination mechanisms in the device. Analyzing fabricated perovskite solar cells using their ideality factor can indicate which type of recombination is dominant in the device. The interaction between the perovskite crystal and transport layers is of high interest as differentials in energy band can hinder overall power conversion efficiency and act as a site for nonradiative recombination loss. We show that measuring the ideality factor of high performing cells and correlating the recombination mechanisms inferred can positively drive the electrochemistry of fabricating these devices. Thereby driving researchers to maximize or minimize types of recombination for optimization.
{"title":"Ideality Factor Based Computational Analysis of Perovskite Solar Cells","authors":"Maniell Workman, D. Z. Chen, S. Musa","doi":"10.1109/SusTech51236.2021.9467471","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467471","url":null,"abstract":"Photovoltaic semiconductors are diodes which produce a current when exposed to light. The ideality factor is a parameter which tells how closely a semiconductor behaves to an ideal diode. In an ideal diode, the only mechanism for hole electron recombination is direct bimolecular recombination. Because there are multiple mechanisms of recombination, there are no real devices with a perfect ideality factor. The types of recombination occurring within a device can be inferred by its ideality factor. In this work, we examine the ideality factor of perovskite solar cells to identify possible recombination mechanisms in the device. Analyzing fabricated perovskite solar cells using their ideality factor can indicate which type of recombination is dominant in the device. The interaction between the perovskite crystal and transport layers is of high interest as differentials in energy band can hinder overall power conversion efficiency and act as a site for nonradiative recombination loss. We show that measuring the ideality factor of high performing cells and correlating the recombination mechanisms inferred can positively drive the electrochemistry of fabricating these devices. Thereby driving researchers to maximize or minimize types of recombination for optimization.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122826693","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 : 2021-04-22DOI: 10.1109/SusTech51236.2021.9467450
Aquib Junaid Razack, Vysyakh Ajith, Rajesh K. Gupta
Traffic Signal Control using Reinforcement Learning has been proved to have potential in alleviating traffic congestion in urban areas. Although research has been conducted in this field, it is still an open challenge to find an effective but low-cost solution to this problem. This paper presents multiple deep reinforcement learning-based traffic signal control systems that can help regulate the flow of traffic at intersections and then compares the results. The proposed systems are coupled with SUMO (Simulation of Urban MObility), an agent-based simulator that provides a realistic environment to explore the outcomes of the models.
{"title":"A Deep Reinforcement Learning Approach to Traffic Signal Control","authors":"Aquib Junaid Razack, Vysyakh Ajith, Rajesh K. Gupta","doi":"10.1109/SusTech51236.2021.9467450","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467450","url":null,"abstract":"Traffic Signal Control using Reinforcement Learning has been proved to have potential in alleviating traffic congestion in urban areas. Although research has been conducted in this field, it is still an open challenge to find an effective but low-cost solution to this problem. This paper presents multiple deep reinforcement learning-based traffic signal control systems that can help regulate the flow of traffic at intersections and then compares the results. The proposed systems are coupled with SUMO (Simulation of Urban MObility), an agent-based simulator that provides a realistic environment to explore the outcomes of the models.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131109470","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 : 2021-04-22DOI: 10.1109/SusTech51236.2021.9467444
Enrique Nueve, R. Jackson, R. Sankaran, N. Ferrier, S. Collis
In addition to natural processes such as photosynthesis and evapotranspiration, net radiation affects industrial applications such as photovoltaic energy management and solar thermal collection. We propose a deep learning approach for nowcasting net radiation within subhourly and intrahour horizons to better understand and control processes influenced by net radiation. Specifically, we developed a deep-learning-based CNN-LSTM, named WeatherNet, that combines multiple local ground-based cameras and weather sensor data to predict net radiation. Unlike previous methodologies, our approach involves images from three different cameras: a sky-facing RGB camera, a horizon-facing RGB camera, and a horizon-facing forward-looking infrared camera. Further, WeatherNet was designed to run "at the edge" using the Waggle edge computing framework to reduce the data bandwidth, improve the latency of predictions, and eliminate centralized data collection. With our proposed dataset and model, WeatherNet, we present a novel methodology using relatively inexpensive equipment for nowcasting net radiation precisely between a 15- and 90-minute horizon.
{"title":"WeatherNet: Nowcasting Net Radiation at the Edge","authors":"Enrique Nueve, R. Jackson, R. Sankaran, N. Ferrier, S. Collis","doi":"10.1109/SusTech51236.2021.9467444","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467444","url":null,"abstract":"In addition to natural processes such as photosynthesis and evapotranspiration, net radiation affects industrial applications such as photovoltaic energy management and solar thermal collection. We propose a deep learning approach for nowcasting net radiation within subhourly and intrahour horizons to better understand and control processes influenced by net radiation. Specifically, we developed a deep-learning-based CNN-LSTM, named WeatherNet, that combines multiple local ground-based cameras and weather sensor data to predict net radiation. Unlike previous methodologies, our approach involves images from three different cameras: a sky-facing RGB camera, a horizon-facing RGB camera, and a horizon-facing forward-looking infrared camera. Further, WeatherNet was designed to run \"at the edge\" using the Waggle edge computing framework to reduce the data bandwidth, improve the latency of predictions, and eliminate centralized data collection. With our proposed dataset and model, WeatherNet, we present a novel methodology using relatively inexpensive equipment for nowcasting net radiation precisely between a 15- and 90-minute horizon.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124916883","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 : 2020-12-30DOI: 10.1109/SusTech51236.2021.9467457
Mukesh Gautam, N. Bhusal, M. Benidris, P. Fajri
As the deployment of zero emission transportation technologies, specifically electric vehicles (EVs), is increasing, the concept of their eco-driving is gaining significant attention. Contrary to the eco-driving techniques used in conventional internal combustion engine vehicles that do not have the capability of regenerative braking, this paper proposes a genetic algorithm (GA)-based eco-driving technique for EVs considering regenerative braking. In the proposed approach, the optimal or near-optimal combination of variables in the driving cycle of EVs is searched using GA. The proposed approach starts by generating an initial population of chromosomes, where all variables under consideration are encoded in each chromosome. This population of chromosomes is passed through crossover, mutation, and elitist-based selection over a certain number of generations, which results in a driving cycle with the least energy consumption. The proposed method is verified using case studies consisting of two types of driving cycles. The results show the capability of the proposed method in computing the minimum energy driving cycle.
{"title":"A GA-based Approach to Eco-driving of Electric Vehicles Considering Regenerative Braking","authors":"Mukesh Gautam, N. Bhusal, M. Benidris, P. Fajri","doi":"10.1109/SusTech51236.2021.9467457","DOIUrl":"https://doi.org/10.1109/SusTech51236.2021.9467457","url":null,"abstract":"As the deployment of zero emission transportation technologies, specifically electric vehicles (EVs), is increasing, the concept of their eco-driving is gaining significant attention. Contrary to the eco-driving techniques used in conventional internal combustion engine vehicles that do not have the capability of regenerative braking, this paper proposes a genetic algorithm (GA)-based eco-driving technique for EVs considering regenerative braking. In the proposed approach, the optimal or near-optimal combination of variables in the driving cycle of EVs is searched using GA. The proposed approach starts by generating an initial population of chromosomes, where all variables under consideration are encoded in each chromosome. This population of chromosomes is passed through crossover, mutation, and elitist-based selection over a certain number of generations, which results in a driving cycle with the least energy consumption. The proposed method is verified using case studies consisting of two types of driving cycles. The results show the capability of the proposed method in computing the minimum energy driving cycle.","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114685998","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 : 1900-01-01DOI: 10.1109/sustech51236.2021.9467429
Milou, Jansen
{"title":"Program by Session","authors":"Milou, Jansen","doi":"10.1109/sustech51236.2021.9467429","DOIUrl":"https://doi.org/10.1109/sustech51236.2021.9467429","url":null,"abstract":"","PeriodicalId":127126,"journal":{"name":"2021 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121231785","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：