Pub Date : 2022-09-26DOI: 10.1109/ISC255366.2022.9922504
Nafiseh Ghorbani-Renani, Philip Odonkor
In this study, we proposed a mixed-integer linear programming model to determine the optimal trading and operational strategies necessary to enable efficient peer-to-peer (P2P) energy trading and resource utilization within fully cooperative community microgrids. The proposed model considers tiered utility tariffs accounting for (i) the time-of-use (TOU) rate and (ii) the level of cumulative consumption. Given the heterogenous mix of prosumers and consumers common in community microgrids, the proposed model seeks to provide decision support for the optimal utilization of generated electricity by determining if it should be self-consumed, stored for future use, curtailed, or traded with peers. Likewise, the proposed approach determines operational strategies for non-prosumer peers with regards to sourcing electricity to satisfy their respective energy deficits. The model presents a scalable approach for energy cost savings for both prosumers and energy consumers regardless of their role in the peer market. To demonstrate this functionality, we leverage the proposed model to solve for the optimal trading strategy within a 5-building community microgrid. Real-world energy demand and generation data pertinent to 5 households in the New York region was sampled using the Pecan Street Inc. Dataport database. Results were compared to that of a traditional centralized grid model. The results highlight the benefits of P2P market design in comparison with the traditional unidirectional grid model. In addition, the outcomes underline that energy consumers satisfy most of their demand from the P2P market during peak hours to obtain greater cost savings.
在这项研究中，我们提出了一个混合整数线性规划模型，以确定在完全合作的社区微电网中实现高效点对点(P2P)能源交易和资源利用所必需的最佳交易和运营策略。拟议的模型考虑了公用事业分层电价，考虑了(i)分时电价(TOU)费率和(ii)累计消费水平。考虑到社区微电网中常见的产消者和消费者的异质组合，所提出的模型试图通过确定是否应该自行消耗、储存以备将来使用、削减或与同行交易来为发电的最佳利用提供决策支持。同样，所提出的方法确定了非生产消费者同行在采购电力以满足各自能源短缺方面的运营策略。该模型为生产消费者和能源消费者提供了一种可扩展的能源成本节约方法，无论他们在对等市场中的角色如何。为了证明这一功能，我们利用所提出的模型来解决5栋建筑社区微电网内的最佳交易策略。使用Pecan Street Inc.对纽约地区5个家庭的实际能源需求和发电数据进行了抽样。Dataport数据库。结果与传统的集中式网格模型进行了比较。与传统的单向网格模型相比，研究结果突出了P2P市场设计的优势。此外，研究结果强调，能源消费者在高峰时段满足P2P市场的大部分需求，以获得更大的成本节约。
{"title":"An Energy Cost Optimization Model for Electricity Trading in Community Microgrids","authors":"Nafiseh Ghorbani-Renani, Philip Odonkor","doi":"10.1109/ISC255366.2022.9922504","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922504","url":null,"abstract":"In this study, we proposed a mixed-integer linear programming model to determine the optimal trading and operational strategies necessary to enable efficient peer-to-peer (P2P) energy trading and resource utilization within fully cooperative community microgrids. The proposed model considers tiered utility tariffs accounting for (i) the time-of-use (TOU) rate and (ii) the level of cumulative consumption. Given the heterogenous mix of prosumers and consumers common in community microgrids, the proposed model seeks to provide decision support for the optimal utilization of generated electricity by determining if it should be self-consumed, stored for future use, curtailed, or traded with peers. Likewise, the proposed approach determines operational strategies for non-prosumer peers with regards to sourcing electricity to satisfy their respective energy deficits. The model presents a scalable approach for energy cost savings for both prosumers and energy consumers regardless of their role in the peer market. To demonstrate this functionality, we leverage the proposed model to solve for the optimal trading strategy within a 5-building community microgrid. Real-world energy demand and generation data pertinent to 5 households in the New York region was sampled using the Pecan Street Inc. Dataport database. Results were compared to that of a traditional centralized grid model. The results highlight the benefits of P2P market design in comparison with the traditional unidirectional grid model. In addition, the outcomes underline that energy consumers satisfy most of their demand from the P2P market during peak hours to obtain greater cost savings.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116811267","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921933
S. Demirel, T. Alskaif, J. Pennings, M. Verhulst, P. Debie, B. Tekinerdogan
This paper proposes a novel framework for energy utility companies to anticipate their customers' energy usage based on their historical consumption data. The proposed framework comprises three major stages: (i) it detects and removes anomalies in consumers' energy consumption data by employing the isolation forest (iForest); (ii) it forms clusters of distinct consumer groups based on similarities in their consumption behavior via the k-means clustering algorithm; and (iii) it predicts electricity consumption by using deep learning algorithms. To this end, two different deep learning algorithms are designed: a long short-term memory (LSTM) network and the combination of convolutional neural network (CNN) and LSTM (referred to as CNN-LSTM) with multiple inputs. Since the latter is a combination of CNN and LSTM models, we apply a 2-D discrete wavelet transform (DWT) based feature extraction to the Gramian angular field (GAF) transformation of the time series to improve the accuracy of predictions. Various evaluation metrics are utilized for 1-hour- and 24-hours-ahead predictions with two different sliding-window sizes, i.e., 24 hours and 36 hours. The results demonstrate that the CNN-LSTM performs significantly better in predicting 24-hours-ahead electricity consumption.
{"title":"A framework for multi-stage ML-based electricity demand forecasting","authors":"S. Demirel, T. Alskaif, J. Pennings, M. Verhulst, P. Debie, B. Tekinerdogan","doi":"10.1109/ISC255366.2022.9921933","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921933","url":null,"abstract":"This paper proposes a novel framework for energy utility companies to anticipate their customers' energy usage based on their historical consumption data. The proposed framework comprises three major stages: (i) it detects and removes anomalies in consumers' energy consumption data by employing the isolation forest (iForest); (ii) it forms clusters of distinct consumer groups based on similarities in their consumption behavior via the k-means clustering algorithm; and (iii) it predicts electricity consumption by using deep learning algorithms. To this end, two different deep learning algorithms are designed: a long short-term memory (LSTM) network and the combination of convolutional neural network (CNN) and LSTM (referred to as CNN-LSTM) with multiple inputs. Since the latter is a combination of CNN and LSTM models, we apply a 2-D discrete wavelet transform (DWT) based feature extraction to the Gramian angular field (GAF) transformation of the time series to improve the accuracy of predictions. Various evaluation metrics are utilized for 1-hour- and 24-hours-ahead predictions with two different sliding-window sizes, i.e., 24 hours and 36 hours. The results demonstrate that the CNN-LSTM performs significantly better in predicting 24-hours-ahead electricity consumption.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"88 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120987740","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921923
F. Villanueva, Cristina Bolaños Peño, A. Rubio, Rubén Cantarero, Jesús Fernández-Bermejo Ruiz, Javier Dorado
One challenge of any smart city is the management of crowded events (concerts, protests, marathons, etc.). For civil servants in charge of management, in-advance attendance prevision, real-time situational awareness and its evolution forecasting are crucial to resource assignment. These massive events put under stress public resources, organization and safety of smart cities. In this paper, we describe an ongoing effort to model urban layout, sensors deployed, and citizen information (from social networks and smartphone application) to deal with these situations. We use the concept of a digital twin applied to a city by modelling different flows of information which are integrated with a 3D virtual representation with forecasting possibilities. The main contribution of this paper is the architecture proposed and GUI using the augmented virtuality concept. The main purpose of our proposal is to facilitate the knowledge of the situation and the management of this type of event.
{"title":"Crowded event management in smart cities using a digital twin approach","authors":"F. Villanueva, Cristina Bolaños Peño, A. Rubio, Rubén Cantarero, Jesús Fernández-Bermejo Ruiz, Javier Dorado","doi":"10.1109/ISC255366.2022.9921923","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921923","url":null,"abstract":"One challenge of any smart city is the management of crowded events (concerts, protests, marathons, etc.). For civil servants in charge of management, in-advance attendance prevision, real-time situational awareness and its evolution forecasting are crucial to resource assignment. These massive events put under stress public resources, organization and safety of smart cities. In this paper, we describe an ongoing effort to model urban layout, sensors deployed, and citizen information (from social networks and smartphone application) to deal with these situations. We use the concept of a digital twin applied to a city by modelling different flows of information which are integrated with a 3D virtual representation with forecasting possibilities. The main contribution of this paper is the architecture proposed and GUI using the augmented virtuality concept. The main purpose of our proposal is to facilitate the knowledge of the situation and the management of this type of event.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115380945","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922572
M. Bessho, Ken Sakamura
Social distancing plays an important role in the control of the spread of infectious diseases. This study proposes a service that forecasts street-level crowd density in the near future. We collected street-level crowd density levels for months during the COVID-19 pandemic by observing public Bluetooth Low Energy advertisements from popular contact tracing applications. We then designed a model to predict crowd density level from other factors such as calendars, weather, and recent trends of crowd density level using Random Forest Regressor. Based on the model, we implemented a crowd density forecast service by incorporating an external weather forecast service, and we published the forecast on our website and a Japanese television program. The experimental results indicate that the model can predict the crowd density for the following week with a coefficient of determination of 0.85 or higher on average, which demonstrates that a practical crowd density forecast can be realized with our method.
{"title":"Design and Implementation of Street-level Crowd Density Forecast using Contact Tracing Applications","authors":"M. Bessho, Ken Sakamura","doi":"10.1109/ISC255366.2022.9922572","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922572","url":null,"abstract":"Social distancing plays an important role in the control of the spread of infectious diseases. This study proposes a service that forecasts street-level crowd density in the near future. We collected street-level crowd density levels for months during the COVID-19 pandemic by observing public Bluetooth Low Energy advertisements from popular contact tracing applications. We then designed a model to predict crowd density level from other factors such as calendars, weather, and recent trends of crowd density level using Random Forest Regressor. Based on the model, we implemented a crowd density forecast service by incorporating an external weather forecast service, and we published the forecast on our website and a Japanese television program. The experimental results indicate that the model can predict the crowd density for the following week with a coefficient of determination of 0.85 or higher on average, which demonstrates that a practical crowd density forecast can be realized with our method.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116514110","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922293
Chao Su, Xiaomei Wu, Yanming Guo, Chun Sing Lai, Liang Xu, Xuan Zhao
With the increasing scale and complexity of powerline construction, the challenges of powerline system operation and maintenance are gradually increasing. The research and application of unmanned aerial vehicle (UAV) Lidar technology for powerline inspections is developing rapidly. The Lidar point cloud and visible light measurement are processed intelligently by the powerline multi-source and heterogeneous data automatic fusion technology. Then the three-dimensional model of the powerline system and electrical equipment is obtained. Consequently, the efficient resolving of point cloud data for powerlines, identification of equipment locations and types are realized. The fast measurement and elaborating modeling of the three-dimensional system for powerlines is obtained, which may effectively and comprehensively show the operation status of powerlines. The point cloud classification algorithm is adopted in this paper. Experimental results demonstrated that the proposed method performed well in the detection accuracy of identification and classification of lines and pylons in a complex environment. The classification accuracies for transmission lines and distribution lines are 97.26% and 95.29% respectively. The average classification accuracies of both lines and pylons are 80.88% and 82.25%, respectively.
{"title":"Automatic Multi-source Data Fusion Technique of Powerline Corridor using UAV Lidar","authors":"Chao Su, Xiaomei Wu, Yanming Guo, Chun Sing Lai, Liang Xu, Xuan Zhao","doi":"10.1109/ISC255366.2022.9922293","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922293","url":null,"abstract":"With the increasing scale and complexity of powerline construction, the challenges of powerline system operation and maintenance are gradually increasing. The research and application of unmanned aerial vehicle (UAV) Lidar technology for powerline inspections is developing rapidly. The Lidar point cloud and visible light measurement are processed intelligently by the powerline multi-source and heterogeneous data automatic fusion technology. Then the three-dimensional model of the powerline system and electrical equipment is obtained. Consequently, the efficient resolving of point cloud data for powerlines, identification of equipment locations and types are realized. The fast measurement and elaborating modeling of the three-dimensional system for powerlines is obtained, which may effectively and comprehensively show the operation status of powerlines. The point cloud classification algorithm is adopted in this paper. Experimental results demonstrated that the proposed method performed well in the detection accuracy of identification and classification of lines and pylons in a complex environment. The classification accuracies for transmission lines and distribution lines are 97.26% and 95.29% respectively. The average classification accuracies of both lines and pylons are 80.88% and 82.25%, respectively.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116467021","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922404
Z. Wang, Shen Wang
Rerouting vehicles for urban congestion avoidance is challenging as the decision has to be undertaken promptly with the consideration of traffic condition changes caused by other vehicles' routing plans. Existing solutions such as the on-board navigation systems (e.g., Google Maps) cannot meet these requirements which is prone to trigger the well-known routing oscillation problem. Though deep reinforcement learning (DRL) approaches are able to provide a high-quality solution and satisfy the real-time requirement, not only do they usually suffer the slow and instability issues for convergence, but the input information, like a picture for each time step, is also teeming with redundant information. In this paper, we propose XRouting model that uses policy-based DRL and the revised Gated Transformer (GTr) architecture to accelerate and stabilize the training convergence in solving dynamic routing problems. Our simulation study validates that compared with existing rerouting solutions, XRouting can achieve higher reductions in travel time, fuel consumption, CO2 emission, and the route length. More importantly, XRouting is capable of determining which features are predominant when vehicles conduct rerouting. This explainable ability of our model can further guide human drivers what features to consider when rerouting manually in real life.
{"title":"XRouting: Explainable Vehicle Rerouting for Urban Road Congestion Avoidance using Deep Reinforcement Learning","authors":"Z. Wang, Shen Wang","doi":"10.1109/ISC255366.2022.9922404","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922404","url":null,"abstract":"Rerouting vehicles for urban congestion avoidance is challenging as the decision has to be undertaken promptly with the consideration of traffic condition changes caused by other vehicles' routing plans. Existing solutions such as the on-board navigation systems (e.g., Google Maps) cannot meet these requirements which is prone to trigger the well-known routing oscillation problem. Though deep reinforcement learning (DRL) approaches are able to provide a high-quality solution and satisfy the real-time requirement, not only do they usually suffer the slow and instability issues for convergence, but the input information, like a picture for each time step, is also teeming with redundant information. In this paper, we propose XRouting model that uses policy-based DRL and the revised Gated Transformer (GTr) architecture to accelerate and stabilize the training convergence in solving dynamic routing problems. Our simulation study validates that compared with existing rerouting solutions, XRouting can achieve higher reductions in travel time, fuel consumption, CO2 emission, and the route length. More importantly, XRouting is capable of determining which features are predominant when vehicles conduct rerouting. This explainable ability of our model can further guide human drivers what features to consider when rerouting manually in real life.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129788680","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9921865
Gábor Kovács, T. Szirányi
Intelligent and autonomous vehicle safety is a rapidly developing field. With the increasing number of electric vehicles as well as following consumer trends, cars are getting quieter and also heavier which may lead to severe traffic accidents. To help avoiding potential dangerous situations leading to accidents, this paper proposes a collision danger model for individual pedestrians that can aid vehicle safety features and help decision making, using only forward facing optical cameras. Multi pedestrian detection and tracking is performed with a fast joint model. Semantic segmentation and classification is used to refine pedestrian contours and find the 3D positions as well as to understand the location context of pedestrians in the environment. Pedestrian position is tracked and orientation is estimated using 2D bounding boxes. The proposed pedestrian danger model is the combination of the awareness estimated from orientation, passing distance estimated from trajectories and location context from the segmentation results.
{"title":"Pedestrian Collision Danger Model using Attention and Location Context","authors":"Gábor Kovács, T. Szirányi","doi":"10.1109/ISC255366.2022.9921865","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9921865","url":null,"abstract":"Intelligent and autonomous vehicle safety is a rapidly developing field. With the increasing number of electric vehicles as well as following consumer trends, cars are getting quieter and also heavier which may lead to severe traffic accidents. To help avoiding potential dangerous situations leading to accidents, this paper proposes a collision danger model for individual pedestrians that can aid vehicle safety features and help decision making, using only forward facing optical cameras. Multi pedestrian detection and tracking is performed with a fast joint model. Semantic segmentation and classification is used to refine pedestrian contours and find the 3D positions as well as to understand the location context of pedestrians in the environment. Pedestrian position is tracked and orientation is estimated using 2D bounding boxes. The proposed pedestrian danger model is the combination of the awareness estimated from orientation, passing distance estimated from trajectories and location context from the segmentation results.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"439 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132894289","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922506
Gustavo F. Silva, D. G. Costa, Thiago C. Jesus
Emergency detection solutions will be employed to early identify one or more critical situations and trigger proper actions in smart cities, potentially preventing the occurrence of disasters. When such systems are constructed around multi-sensor emergencies detection units, the heterogeneity of monitoring scenarios and eventual requisites changes may demand their reconfiguration to attend new sensing requirements. In this context, this paper proposes a new development framework to guide the programming and operation of multi-sensor detection units that are able to be reconfigured in real time. Moreover, supportive networked elements and interaction messages are proposed within this framework to allow flexible reconfiguration requests in a distributed and scalable way. The required specifications and expected evaluation results are also discussed in this paper.
{"title":"A Framework for the Development of Reconfigurable Sensors-based Emergencies Detection Units in Smart Cities","authors":"Gustavo F. Silva, D. G. Costa, Thiago C. Jesus","doi":"10.1109/ISC255366.2022.9922506","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922506","url":null,"abstract":"Emergency detection solutions will be employed to early identify one or more critical situations and trigger proper actions in smart cities, potentially preventing the occurrence of disasters. When such systems are constructed around multi-sensor emergencies detection units, the heterogeneity of monitoring scenarios and eventual requisites changes may demand their reconfiguration to attend new sensing requirements. In this context, this paper proposes a new development framework to guide the programming and operation of multi-sensor detection units that are able to be reconfigured in real time. Moreover, supportive networked elements and interaction messages are proposed within this framework to allow flexible reconfiguration requests in a distributed and scalable way. The required specifications and expected evaluation results are also discussed in this paper.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133723001","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922226
Animesh Mehta, Gayatri Doctor, Anita Kane, D. Sawant
The concept of Carbon Neutrality is gaining momentum in recent years due to the rising awareness of climate change. Carbon neutrality means (A) Minimize greenhouse gas emissions to the best extent possible, and (B) Create a sink for the residual GHG emissions. Tree plantation being the most effective way for creating natural carbon sinks. The overall objective of this study is reducing the carbon footprint of an educational and research institution in India. The study starts with the assessment of carbon emissions covering scope 1, scope 2 and scope 3 for the selected site. The emissions are quantified keeping in mind the inclusions and exclusions of the study. It further looks at carbon offsets/sinks and the impact that they have on the campus. The study compares data from different years and recommends the way forward towards the achievement of carbon neutrality. This study aims to act as a framework for similar studies for campuses who take a step towards sustainability.
{"title":"Study for achieving carbon-neutral campus in India","authors":"Animesh Mehta, Gayatri Doctor, Anita Kane, D. Sawant","doi":"10.1109/ISC255366.2022.9922226","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922226","url":null,"abstract":"The concept of Carbon Neutrality is gaining momentum in recent years due to the rising awareness of climate change. Carbon neutrality means (A) Minimize greenhouse gas emissions to the best extent possible, and (B) Create a sink for the residual GHG emissions. Tree plantation being the most effective way for creating natural carbon sinks. The overall objective of this study is reducing the carbon footprint of an educational and research institution in India. The study starts with the assessment of carbon emissions covering scope 1, scope 2 and scope 3 for the selected site. The emissions are quantified keeping in mind the inclusions and exclusions of the study. It further looks at carbon offsets/sinks and the impact that they have on the campus. The study compares data from different years and recommends the way forward towards the achievement of carbon neutrality. This study aims to act as a framework for similar studies for campuses who take a step towards sustainability.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130068988","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 : 2022-09-26DOI: 10.1109/ISC255366.2022.9922242
Ahmed Idries, J. Krogstie, Jayaprakash Rajasekharan
Distributed ledger technologies (DLTs) have become a game changer in electrical services platformization and digitalization. Therefore, the need for DLTs in electrical energy services must be understood. We present a case study of a European Union (EU) project in the Norwegian city of Trondheim, where a DLT-driven energy marketplace was piloted. We contribute to the literature and field by presenting the factors, challenges, and issues affecting DLT implementation in electrical energy services, which can be helpful for further work in electrical energy services and platform ecosystems. For policy makers and practitioners, this paper presents DLT providers' reflections about their experience in an electrical energy services project in the smart city context. These insights could be useful to ease future adoption of DLTs and to provide a ground for future empirical investigations.
{"title":"Evaluation of Distributed Ledger Technology Implementation in Electrical Energy Service through a Case Study","authors":"Ahmed Idries, J. Krogstie, Jayaprakash Rajasekharan","doi":"10.1109/ISC255366.2022.9922242","DOIUrl":"https://doi.org/10.1109/ISC255366.2022.9922242","url":null,"abstract":"Distributed ledger technologies (DLTs) have become a game changer in electrical services platformization and digitalization. Therefore, the need for DLTs in electrical energy services must be understood. We present a case study of a European Union (EU) project in the Norwegian city of Trondheim, where a DLT-driven energy marketplace was piloted. We contribute to the literature and field by presenting the factors, challenges, and issues affecting DLT implementation in electrical energy services, which can be helpful for further work in electrical energy services and platform ecosystems. For policy makers and practitioners, this paper presents DLT providers' reflections about their experience in an electrical energy services project in the smart city context. These insights could be useful to ease future adoption of DLTs and to provide a ground for future empirical investigations.","PeriodicalId":277015,"journal":{"name":"2022 IEEE International Smart Cities Conference (ISC2)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124524181","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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