Pub Date : 2016-04-20DOI: 10.1109/SGCF.2016.7492433
B. Alagoz, S. Alagoz, B. Baran
Ongoing migration trend to urban areas populates cities and leads to urbanization problems. A major problem of crowded cities is human mobility. Growing urban population and increase of personal automobiles in traffic lead to energy inefficiency in daily transportation activities and result in emission of more heat and pollutant in urban areas. Energy inefficiency in motorized transportation has negative effects on welfare of citizen, public health, air quality and economy of cities. More carbon emission and related global climate changes are global effects of energy inefficiency in transportation. This study presents an analysis of transportation energy consumption based on average mass mobility. Weighting average function is used to statistically represent transportation mode and social gender patterns of cities. Analysis examples are illustrated and solutions for improvement of energy efficiency are discussed in the aspect of human mobility in urban areas. The study can be useful for evaluation of transportation efficiency.
{"title":"Energy consumption analysis of motorized transportation in cities by considering average mobile mass","authors":"B. Alagoz, S. Alagoz, B. Baran","doi":"10.1109/SGCF.2016.7492433","DOIUrl":"https://doi.org/10.1109/SGCF.2016.7492433","url":null,"abstract":"Ongoing migration trend to urban areas populates cities and leads to urbanization problems. A major problem of crowded cities is human mobility. Growing urban population and increase of personal automobiles in traffic lead to energy inefficiency in daily transportation activities and result in emission of more heat and pollutant in urban areas. Energy inefficiency in motorized transportation has negative effects on welfare of citizen, public health, air quality and economy of cities. More carbon emission and related global climate changes are global effects of energy inefficiency in transportation. This study presents an analysis of transportation energy consumption based on average mass mobility. Weighting average function is used to statistically represent transportation mode and social gender patterns of cities. Analysis examples are illustrated and solutions for improvement of energy efficiency are discussed in the aspect of human mobility in urban areas. The study can be useful for evaluation of transportation efficiency.","PeriodicalId":403426,"journal":{"name":"2016 4th International Istanbul Smart Grid Congress and Fair (ICSG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131010307","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 : 2016-04-20DOI: 10.1109/SGCF.2016.7492439
Akbar Majidi, Hüseyin Polat, Aydın Çetin
Finding a vacant place to park cars in the rush hour is time-consuming and even may be frustrating for the drivers. In some studies, vehicles are equipped with communicative tools called On Board Units (OBUs), which come along with roadside devices known as Roadside Units (RSUs), which allow the drivers to communicate each other and trace a vacant parking place easily. Previous systems work with connection of sensors all over the road and parking space and may result in spending much time to find a parking place, occupancy of the empty parking place until the car gets to the desired location, requirement for an additional hardware, wired network communication, and security issues. In this paper, we propose an exponential smoothing and multi-objective decision-making by using cloud-based methods to find the best parking place, taking into consideration of the park-cost for the driver. The proposed system uses the cellular base stations to eliminate the cost of the RSUs and the sensors. The results of our simulations via NS-2 network simulator confirm the efficiency of the proposed model.
{"title":"Finding a best parking place using exponential smoothing and cloud system in a metropolitan area","authors":"Akbar Majidi, Hüseyin Polat, Aydın Çetin","doi":"10.1109/SGCF.2016.7492439","DOIUrl":"https://doi.org/10.1109/SGCF.2016.7492439","url":null,"abstract":"Finding a vacant place to park cars in the rush hour is time-consuming and even may be frustrating for the drivers. In some studies, vehicles are equipped with communicative tools called On Board Units (OBUs), which come along with roadside devices known as Roadside Units (RSUs), which allow the drivers to communicate each other and trace a vacant parking place easily. Previous systems work with connection of sensors all over the road and parking space and may result in spending much time to find a parking place, occupancy of the empty parking place until the car gets to the desired location, requirement for an additional hardware, wired network communication, and security issues. In this paper, we propose an exponential smoothing and multi-objective decision-making by using cloud-based methods to find the best parking place, taking into consideration of the park-cost for the driver. The proposed system uses the cellular base stations to eliminate the cost of the RSUs and the sensors. The results of our simulations via NS-2 network simulator confirm the efficiency of the proposed model.","PeriodicalId":403426,"journal":{"name":"2016 4th International Istanbul Smart Grid Congress and Fair (ICSG)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128214520","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 : 2016-04-20DOI: 10.1109/SGCF.2016.7492436
M. E. Cebeci, O. B. Tör, Seyit Cem Yılmaz, Ozan Güreç, Okan Benli
Smart grids require not only smart tools to automatize grid operation and planning processes but also require smart approaches to maximize utilization of the capabilities of those tools as well. Geographic information systems (GIS) provide an integrated suite of software for visualizing the system data, tools for network optimisation, and a range of automation and information processing systems which assist in the operation, maintenance and planning of distribution networks. Interfacing of GIS data with sophisticated power system simulation tools facilitates model updating process for the network planner who needs updated network data for operational and planning analysis. Quality and quantity of data in GIS database generally limit the planners. This paper presents a smart process for interfacing GIS based database with power system simulation tools, based on the lessons learnt from interfacing ArcGIS with DIgSILENT PowerFactory at Başkent DISCO.
{"title":"Lessons learnt from interfacing ArcGIS and DIgSILENT powerfactory at Başkent DISCO","authors":"M. E. Cebeci, O. B. Tör, Seyit Cem Yılmaz, Ozan Güreç, Okan Benli","doi":"10.1109/SGCF.2016.7492436","DOIUrl":"https://doi.org/10.1109/SGCF.2016.7492436","url":null,"abstract":"Smart grids require not only smart tools to automatize grid operation and planning processes but also require smart approaches to maximize utilization of the capabilities of those tools as well. Geographic information systems (GIS) provide an integrated suite of software for visualizing the system data, tools for network optimisation, and a range of automation and information processing systems which assist in the operation, maintenance and planning of distribution networks. Interfacing of GIS data with sophisticated power system simulation tools facilitates model updating process for the network planner who needs updated network data for operational and planning analysis. Quality and quantity of data in GIS database generally limit the planners. This paper presents a smart process for interfacing GIS based database with power system simulation tools, based on the lessons learnt from interfacing ArcGIS with DIgSILENT PowerFactory at Başkent DISCO.","PeriodicalId":403426,"journal":{"name":"2016 4th International Istanbul Smart Grid Congress and Fair (ICSG)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117126609","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 : 2016-04-20DOI: 10.1109/SGCF.2016.7492424
Mohammed Mahdi, V. M. Istemihan Genc
In this paper, a preventive control method against transient instabilities due to a set of critical contingencies that could occur in a large power system is proposed. The generation rescheduling, adopted as the preventive control, is based on the coherency between the generators. The rescheduling is done by attempting to bring the generators’ rotor speeds equal after a three phase fault that might cause instability. The proposed methodology involves off-line simulations to determine the system response and to check the severity of each contingency. For each critical contingency, a scaling factor is assigned to scale the speed trajectory of the contingency. Active-set sequential quadratic programming (SQP) is used to optimize the scaling factors in such a way that the rescheduling method based on the scaling factors improves the dynamic security and restores the system’s stability for all contingencies that are taken into account.
{"title":"A coherency based generation rescheduling against multiple contingencies","authors":"Mohammed Mahdi, V. M. Istemihan Genc","doi":"10.1109/SGCF.2016.7492424","DOIUrl":"https://doi.org/10.1109/SGCF.2016.7492424","url":null,"abstract":"In this paper, a preventive control method against transient instabilities due to a set of critical contingencies that could occur in a large power system is proposed. The generation rescheduling, adopted as the preventive control, is based on the coherency between the generators. The rescheduling is done by attempting to bring the generators’ rotor speeds equal after a three phase fault that might cause instability. The proposed methodology involves off-line simulations to determine the system response and to check the severity of each contingency. For each critical contingency, a scaling factor is assigned to scale the speed trajectory of the contingency. Active-set sequential quadratic programming (SQP) is used to optimize the scaling factors in such a way that the rescheduling method based on the scaling factors improves the dynamic security and restores the system’s stability for all contingencies that are taken into account.","PeriodicalId":403426,"journal":{"name":"2016 4th International Istanbul Smart Grid Congress and Fair (ICSG)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116555731","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 : 2016-04-20DOI: 10.1109/SGCF.2016.7492437
Oguzhan Oktay Buyuk, Sevgi Nur Bilgin
In this paper, a novel system application to recover electricity losses using an unsupervised learning, self-learning mapping mechanism is introduced. Actually, energy and its transmission are becoming a vital issue for both the economy and the environment. Considering many devices in our world run on electricity, it is now important to keep up with how we can obtain maximum energy efficiency in electricity transmission by reducing losses and leakage. A new system application and module approach can communicate with electricity transmission lines to define and track energy losses. In this study, we examine how the system uses unsupervised learning to find the best transmission path to follow. This application is designed to interconnect with electricity transmission line on smart grids. This system also has critical recovering on CO2 emissions occurring on routing correct plan, notification integration which may prepare a report to the network nodes by itself.
{"title":"A novel application to increase energy efficiency using artificial neural networks","authors":"Oguzhan Oktay Buyuk, Sevgi Nur Bilgin","doi":"10.1109/SGCF.2016.7492437","DOIUrl":"https://doi.org/10.1109/SGCF.2016.7492437","url":null,"abstract":"In this paper, a novel system application to recover electricity losses using an unsupervised learning, self-learning mapping mechanism is introduced. Actually, energy and its transmission are becoming a vital issue for both the economy and the environment. Considering many devices in our world run on electricity, it is now important to keep up with how we can obtain maximum energy efficiency in electricity transmission by reducing losses and leakage. A new system application and module approach can communicate with electricity transmission lines to define and track energy losses. In this study, we examine how the system uses unsupervised learning to find the best transmission path to follow. This application is designed to interconnect with electricity transmission line on smart grids. This system also has critical recovering on CO2 emissions occurring on routing correct plan, notification integration which may prepare a report to the network nodes by itself.","PeriodicalId":403426,"journal":{"name":"2016 4th International Istanbul Smart Grid Congress and Fair (ICSG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129114225","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 : 2016-04-20DOI: 10.1109/SGCF.2016.7492422
B. Akbal
The sheath current generates on metal sheath of underground cable, and it causes cable faults, electroshock risk and reducing of cable performance in high voltage underground cable line. Therefore, the sheath current must be reduced, and if the sheath current can be determined before high voltage underground line is installed, the required precautions can be taken to reduce the sheath current. Hence, cable faults and electroshock risk are prevented, and cable performance increases. In this study, simulations of high voltage underground cable line are made in PSCAD/EMTDC, and the sheath current is forecasted by using hybrid artificial neural network (ANN) method. Differential evolution algorithm (DEA) and particle swarm optimization (PSO) are used to generate hybrid ANN method. The results of hybrid ANN method are better than classic ANN, and the results of DEA-ANN method are better than PSO-ANN. Also DEA-ANN can be used in forecasting studies.
{"title":"Hybrid ANN methods to reduce the sheath current effects in high voltage underground cable line","authors":"B. Akbal","doi":"10.1109/SGCF.2016.7492422","DOIUrl":"https://doi.org/10.1109/SGCF.2016.7492422","url":null,"abstract":"The sheath current generates on metal sheath of underground cable, and it causes cable faults, electroshock risk and reducing of cable performance in high voltage underground cable line. Therefore, the sheath current must be reduced, and if the sheath current can be determined before high voltage underground line is installed, the required precautions can be taken to reduce the sheath current. Hence, cable faults and electroshock risk are prevented, and cable performance increases. In this study, simulations of high voltage underground cable line are made in PSCAD/EMTDC, and the sheath current is forecasted by using hybrid artificial neural network (ANN) method. Differential evolution algorithm (DEA) and particle swarm optimization (PSO) are used to generate hybrid ANN method. The results of hybrid ANN method are better than classic ANN, and the results of DEA-ANN method are better than PSO-ANN. Also DEA-ANN can be used in forecasting studies.","PeriodicalId":403426,"journal":{"name":"2016 4th International Istanbul Smart Grid Congress and Fair (ICSG)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121338544","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 : 2016-04-20DOI: 10.1109/SGCF.2016.7492420
Ö. F. Ertugrul, Y. Kaya
Estimation of energy efficiency is one of the major issues in smart city planning. Although, there are some papers about estimation of energy efficiency of the buildings, there is still a requirement of an effective method that can be used in all climatic zones. Therefore, extreme learning method (ELM), which is a training method for single hidden layer neural network, was employed in the dataset that contains the properties of buildings such as shape, area and height and cooling and heating loads were calculated. Achieved results by ELM were compared with the results in the literature and the results obtained by some popular machine learning methods such as artificial neural network, linear regression, and etc. Obtained results by ELM found acceptable.
{"title":"Smart city planning by estimating energy efficiency of buildings by extreme learning machine","authors":"Ö. F. Ertugrul, Y. Kaya","doi":"10.1109/SGCF.2016.7492420","DOIUrl":"https://doi.org/10.1109/SGCF.2016.7492420","url":null,"abstract":"Estimation of energy efficiency is one of the major issues in smart city planning. Although, there are some papers about estimation of energy efficiency of the buildings, there is still a requirement of an effective method that can be used in all climatic zones. Therefore, extreme learning method (ELM), which is a training method for single hidden layer neural network, was employed in the dataset that contains the properties of buildings such as shape, area and height and cooling and heating loads were calculated. Achieved results by ELM were compared with the results in the literature and the results obtained by some popular machine learning methods such as artificial neural network, linear regression, and etc. Obtained results by ELM found acceptable.","PeriodicalId":403426,"journal":{"name":"2016 4th International Istanbul Smart Grid Congress and Fair (ICSG)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129078064","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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