Early diagnosis of brain tumor plays an important factor in extending the life expectancy of a patient. Therefore, an accurate and timely diagnosis of the type of brain tumor will allow adequate treatment planning and medical assistance. Radiologists commonly use magnetic resonance imaging (MRI) scans to detect and classify brain tumors. The current methods used in the medical field for diagnosis are time-consuming and prone to human error. In recent years, researchers have developed automated techniques for the segmentation and classification of MRI images resulting in a faster diagnosis process. Recent advancements in deep learning have shown greater efficiency in image recognition and classification tasks. In this paper, a convolutional neural network (CNN) (a widely used deep learning architecture for image classification tasks) is developed to classify MRI images into four brain tumor categories. Data augmentation is applied to the training dataset to generalize the images and avoid overfitting problem. Additionally, this paper compares the performance of various pre-trained models such as Vision Transformer (VIT), VGG19, ResNet50, Inception V3, and AlexNet50 with that of the proposed model. Each experiment then explores transfer learning techniques like fine-tuning and freezing layers. In the study, the proposed model yields the most efficient results with a classification accuracy of 94.72%.
{"title":"A CNN-based Approach for Multi-Classification of Brain Tumors","authors":"Sahiti Nallamolu, Hritik Nandanwar, Anurag Singh, Subalalitha C.N.","doi":"10.1109/ASIANCON55314.2022.9908994","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908994","url":null,"abstract":"Early diagnosis of brain tumor plays an important factor in extending the life expectancy of a patient. Therefore, an accurate and timely diagnosis of the type of brain tumor will allow adequate treatment planning and medical assistance. Radiologists commonly use magnetic resonance imaging (MRI) scans to detect and classify brain tumors. The current methods used in the medical field for diagnosis are time-consuming and prone to human error. In recent years, researchers have developed automated techniques for the segmentation and classification of MRI images resulting in a faster diagnosis process. Recent advancements in deep learning have shown greater efficiency in image recognition and classification tasks. In this paper, a convolutional neural network (CNN) (a widely used deep learning architecture for image classification tasks) is developed to classify MRI images into four brain tumor categories. Data augmentation is applied to the training dataset to generalize the images and avoid overfitting problem. Additionally, this paper compares the performance of various pre-trained models such as Vision Transformer (VIT), VGG19, ResNet50, Inception V3, and AlexNet50 with that of the proposed model. Each experiment then explores transfer learning techniques like fine-tuning and freezing layers. In the study, the proposed model yields the most efficient results with a classification accuracy of 94.72%.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133832027","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909372
Poorani Marimuthu
Automatic abnormality detection in human health status based on the variation in the vital health parameters is a continuous research thrust area. After Covid pandemic the importance of checking the variation in the health status become a part of our regular activities. With the help of artificial intelligence, today many research works have been proposed in abnormality detection. The proposed work is personalized abnormality detection technique based on adaptive unsupervised mechanism and tries to map the health status with the incoming health stream data. The proposed adaptive density-based K-Means fixes the severity range of each vital health parameter of a person and achieved an accuracy rate in fixing the severity range with 91.3% during training and 87.8 % testing respectively.
{"title":"Linguistic Severity Range Fixation of Vital Signs Using Unsupervised Approach in RHM","authors":"Poorani Marimuthu","doi":"10.1109/ASIANCON55314.2022.9909372","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909372","url":null,"abstract":"Automatic abnormality detection in human health status based on the variation in the vital health parameters is a continuous research thrust area. After Covid pandemic the importance of checking the variation in the health status become a part of our regular activities. With the help of artificial intelligence, today many research works have been proposed in abnormality detection. The proposed work is personalized abnormality detection technique based on adaptive unsupervised mechanism and tries to map the health status with the incoming health stream data. The proposed adaptive density-based K-Means fixes the severity range of each vital health parameter of a person and achieved an accuracy rate in fixing the severity range with 91.3% during training and 87.8 % testing respectively.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131497468","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909212
M. Sajjan, Lingangouda Kulkarni, B. Anami, N. B. Gaddagimath
Chilli, one of the spice produce, needs grading before being marketed for produce quality assurance. Manual chilli grading involves high labour cost, time-consuming, inconsistent, and expensive warranting technology intervention. In this work, a non-destructive approach to identify dry chilli images into three levels as good quality, medium quality and poor quality, using a deep learning architectures and grade them are adopted to reduce computation overload. The database of chilli grown in North Karnataka region is prepared as no standard chilli datasets are available. Dry chilli images dataset are augmented to train the dataset for transfer learning (DL) models, namely VGG16, ResNet and EfficientNet-D0 to analyse suitability of good model for the grading of chilli images. Further, work needs integration of the algorithm into automatic chilli grading tool. The proposed EfficentDet model is found suitable and yielded accuracy rate of 95.62% were in VGG16 and Resnet models accuracy was 82.67% and 83.88%. EfficientDet model out performs in terms of grading the dry chilli images.
{"title":"Chilli Identification and Grading in pre/post-harvest Environment based on Computer vision and Deep Learning approaches","authors":"M. Sajjan, Lingangouda Kulkarni, B. Anami, N. B. Gaddagimath","doi":"10.1109/ASIANCON55314.2022.9909212","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909212","url":null,"abstract":"Chilli, one of the spice produce, needs grading before being marketed for produce quality assurance. Manual chilli grading involves high labour cost, time-consuming, inconsistent, and expensive warranting technology intervention. In this work, a non-destructive approach to identify dry chilli images into three levels as good quality, medium quality and poor quality, using a deep learning architectures and grade them are adopted to reduce computation overload. The database of chilli grown in North Karnataka region is prepared as no standard chilli datasets are available. Dry chilli images dataset are augmented to train the dataset for transfer learning (DL) models, namely VGG16, ResNet and EfficientNet-D0 to analyse suitability of good model for the grading of chilli images. Further, work needs integration of the algorithm into automatic chilli grading tool. The proposed EfficentDet model is found suitable and yielded accuracy rate of 95.62% were in VGG16 and Resnet models accuracy was 82.67% and 83.88%. EfficientDet model out performs in terms of grading the dry chilli images.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131533467","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9908741
Aditya Prajapati, Sweta Tripathi
Technology is a way to live life differently. Filters are frequently used in communication systems as it helps in plummeting degradation in communication channel. Microstrip technologies are well-known, that are commonly utilized due to their small size and ease of implementation on printed circuit boards. In this manuscript, a microstrip passive low band pass filter (MPLPF) of measurement (20x15x1.6) mm3 for maximally flat response by using stepped impedance technique is presented. The framework of the filter is simulated and analyzed in Ansys HFSS software. It provides fine performance for 1 GHz – 4.72 GHz frequency range antennas. It has the fabulous results. The low VSWR value guarantees that it performs well. The MPLPF is designed such that it can be operate at sub-6 GHz frequency ranges.
{"title":"Microstrip Passive Low Pass Filter (MPLPF) for Maximally Flat Response","authors":"Aditya Prajapati, Sweta Tripathi","doi":"10.1109/ASIANCON55314.2022.9908741","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908741","url":null,"abstract":"Technology is a way to live life differently. Filters are frequently used in communication systems as it helps in plummeting degradation in communication channel. Microstrip technologies are well-known, that are commonly utilized due to their small size and ease of implementation on printed circuit boards. In this manuscript, a microstrip passive low band pass filter (MPLPF) of measurement (20x15x1.6) mm3 for maximally flat response by using stepped impedance technique is presented. The framework of the filter is simulated and analyzed in Ansys HFSS software. It provides fine performance for 1 GHz – 4.72 GHz frequency range antennas. It has the fabulous results. The low VSWR value guarantees that it performs well. The MPLPF is designed such that it can be operate at sub-6 GHz frequency ranges.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131250858","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909430
L. L. Glória, S. B. Righetto, D. B. S. de Oliveira, M. A. I. Martins, R. A. S. Kraemer, M. A. Ludwig
Electric power systems have undergone several transformations, especially leveraged by the trends of digitalization, decarbonization and decentralization of the electric sector. Following the trends of decarbonization and decentralization, the increased penetration of distributed resources in the electricity grid brings new challenges and opportunities for system management. In terms of digitization, the advent of microgrids and virtual power plants stands out as possibilities for aggregating and managing these resources. Thus, the integration of distributed generation, microgrids and virtual power plants presents not only new market opportunities, but also new regulatory and technological challenges for the electric system, since they change the way such entities interact with each other and with the generation, transmission, distribution and commercialization systems of electric energy, directly or indirectly impacting these sectors. In order to contribute to the discussion of topics relevant to these challenges, the present work aims to investigate possibilities for the integration of microgrids and virtual power plants. With this objective, a bibliographic mapping was carried out in order to elucidate concepts relevant to microgrids, virtual power plants and the possibilities of their integration. These themes are presented throughout the work, as well as regulatory aspects and suggestions for future research.
{"title":"Microgrids and Virtual Power Plants: Integration Possibilities","authors":"L. L. Glória, S. B. Righetto, D. B. S. de Oliveira, M. A. I. Martins, R. A. S. Kraemer, M. A. Ludwig","doi":"10.1109/ASIANCON55314.2022.9909430","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909430","url":null,"abstract":"Electric power systems have undergone several transformations, especially leveraged by the trends of digitalization, decarbonization and decentralization of the electric sector. Following the trends of decarbonization and decentralization, the increased penetration of distributed resources in the electricity grid brings new challenges and opportunities for system management. In terms of digitization, the advent of microgrids and virtual power plants stands out as possibilities for aggregating and managing these resources. Thus, the integration of distributed generation, microgrids and virtual power plants presents not only new market opportunities, but also new regulatory and technological challenges for the electric system, since they change the way such entities interact with each other and with the generation, transmission, distribution and commercialization systems of electric energy, directly or indirectly impacting these sectors. In order to contribute to the discussion of topics relevant to these challenges, the present work aims to investigate possibilities for the integration of microgrids and virtual power plants. With this objective, a bibliographic mapping was carried out in order to elucidate concepts relevant to microgrids, virtual power plants and the possibilities of their integration. These themes are presented throughout the work, as well as regulatory aspects and suggestions for future research.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133503904","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9908642
Suny Sadik, J. Benedetti, S. Gokhale
This paper computationally analyzes and classifies social media dialogue on climate change based on the tweets collected and annotated during the California wildfires using a three-pronged approach. Opinions and thoughts of climate change supporters and deniers are mined through word cloud visualizations. This reveals that in the climate change debate politics and science is intertwined, with supporters stressing the imminence of climate change, and deniers deflecting it with conspiracy theories, and alternative explanations. Analysis of the metadata offers insights into how the supporting and denying tweets are being received and how they may be spread. This analysis indicates that tweets supporting climate change are shared from verified accounts, and are liked and retweeted many times, whereas those denying climate change circulate through close, like-minded communities. Sophisticated features that consider sarcasm, offensive language, emotions, and engagement are then built into a classification framework that also accounts for class imbalance. This framework can distinguish between tweets that support and deny climate change with a F1-score and accuracy of around 0.90, outperforming contemporary approaches by over 10%. By the virtue of identifying tweets that deny climate change, along with their associated justifications, the paper opens opportunities to design and disseminate educational, scientific content that can persuade the skeptics to abandon their stance.
{"title":"Analyzing Climate Change Dialogue During California Wildfires","authors":"Suny Sadik, J. Benedetti, S. Gokhale","doi":"10.1109/ASIANCON55314.2022.9908642","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9908642","url":null,"abstract":"This paper computationally analyzes and classifies social media dialogue on climate change based on the tweets collected and annotated during the California wildfires using a three-pronged approach. Opinions and thoughts of climate change supporters and deniers are mined through word cloud visualizations. This reveals that in the climate change debate politics and science is intertwined, with supporters stressing the imminence of climate change, and deniers deflecting it with conspiracy theories, and alternative explanations. Analysis of the metadata offers insights into how the supporting and denying tweets are being received and how they may be spread. This analysis indicates that tweets supporting climate change are shared from verified accounts, and are liked and retweeted many times, whereas those denying climate change circulate through close, like-minded communities. Sophisticated features that consider sarcasm, offensive language, emotions, and engagement are then built into a classification framework that also accounts for class imbalance. This framework can distinguish between tweets that support and deny climate change with a F1-score and accuracy of around 0.90, outperforming contemporary approaches by over 10%. By the virtue of identifying tweets that deny climate change, along with their associated justifications, the paper opens opportunities to design and disseminate educational, scientific content that can persuade the skeptics to abandon their stance.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"59 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133616244","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909068
Shushrut Kumar, V. Jagannath, P. Visalakshi
In the unpredictable world of football, primitive techniques are used to evaluate player performances, recruitment, and to build strategies for opponents. We aim to offer an improved and advanced technique of using predictive data like expected goals rather than descriptive data like shots taken and goals scored to analyse the game. Our goal is to judge teams and players on the basis of their performances instead of the results and generated predictive data for scouting and strategy formation. This was achieved by using fixed parameters on machine learning algorithms. The expected goals method gives a number between 0 and 1 for every shot taken, that number is interpreted as the probability of that shot being converted to goal. So if a shot at a particular location and at a certain angle produces an expected goal value off 0.67 then the probability of that shot to be a goal will be 67% meaning if 100 shots are taken from that same position and angle, 67 of those shots will result in goal and 33 shots will not be converted to goal. This method of using predictive data like expected goals is better than using primitive and orthodox descriptive data like total shots taken and shot on target ratio because this strips down the luck factor and just focuses on pure skill and ability. This will be beneficial while finding out players with real and hidden talents as well as analysing performances in an unbiased manner without the influence of final result.
{"title":"Quantifying Shot Quality and Predicting the Goal Probability for Football Shots","authors":"Shushrut Kumar, V. Jagannath, P. Visalakshi","doi":"10.1109/ASIANCON55314.2022.9909068","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909068","url":null,"abstract":"In the unpredictable world of football, primitive techniques are used to evaluate player performances, recruitment, and to build strategies for opponents. We aim to offer an improved and advanced technique of using predictive data like expected goals rather than descriptive data like shots taken and goals scored to analyse the game. Our goal is to judge teams and players on the basis of their performances instead of the results and generated predictive data for scouting and strategy formation. This was achieved by using fixed parameters on machine learning algorithms. The expected goals method gives a number between 0 and 1 for every shot taken, that number is interpreted as the probability of that shot being converted to goal. So if a shot at a particular location and at a certain angle produces an expected goal value off 0.67 then the probability of that shot to be a goal will be 67% meaning if 100 shots are taken from that same position and angle, 67 of those shots will result in goal and 33 shots will not be converted to goal. This method of using predictive data like expected goals is better than using primitive and orthodox descriptive data like total shots taken and shot on target ratio because this strips down the luck factor and just focuses on pure skill and ability. This will be beneficial while finding out players with real and hidden talents as well as analysing performances in an unbiased manner without the influence of final result.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128842195","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909335
Hemin Ismael Azeez
This work presents two small antennas for Internet of Things (IoT) based radio collar tracking systems. The antennas are flexible and adhesive that could be installed on the inner surface of the tracker enclosure. The low-profile antennas support LTE1800 cellular communications and global navigation satellite system (GNSS) applications. The tracker device has a compact low profile of 33x33x14mm3.
{"title":"Embedded Flexible Multi Antennas for Radio Collar IoT Applications","authors":"Hemin Ismael Azeez","doi":"10.1109/ASIANCON55314.2022.9909335","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909335","url":null,"abstract":"This work presents two small antennas for Internet of Things (IoT) based radio collar tracking systems. The antennas are flexible and adhesive that could be installed on the inner surface of the tracker enclosure. The low-profile antennas support LTE1800 cellular communications and global navigation satellite system (GNSS) applications. The tracker device has a compact low profile of 33x33x14mm3.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127838468","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909293
Sunil K, Sivagamasundari G
Apache Spark is a widely used efficient distributed computing framework in the field of Big Data for data processing and analytics at a large scale. There is wide demand from organizations to apply deep learning technologies to their existing big data analysis pipelines which will reduce the cost of maintaining additional computational resources. To classify large scale image data is a hot topic. For image classification, the classic Convolution neural network (CNN) model has been widely used as a standard deep learning algorithm. This paper focuses on implementation and demonstrates the execution of combination of Apache Spark and Convolution neural network algorithm that will provide significant improvement in performance for the image classification model. The paper aims to reduce overheads involved in this approach to provide better performance by the usage of novel opensource frameworks and bring together a unified pipeline for the same. Improvements in various performance metrics that are obtained from our experimental setup are presented in this work accordingly.
{"title":"Improvement in Performance of Image Classification based on Apache Spark","authors":"Sunil K, Sivagamasundari G","doi":"10.1109/ASIANCON55314.2022.9909293","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909293","url":null,"abstract":"Apache Spark is a widely used efficient distributed computing framework in the field of Big Data for data processing and analytics at a large scale. There is wide demand from organizations to apply deep learning technologies to their existing big data analysis pipelines which will reduce the cost of maintaining additional computational resources. To classify large scale image data is a hot topic. For image classification, the classic Convolution neural network (CNN) model has been widely used as a standard deep learning algorithm. This paper focuses on implementation and demonstrates the execution of combination of Apache Spark and Convolution neural network algorithm that will provide significant improvement in performance for the image classification model. The paper aims to reduce overheads involved in this approach to provide better performance by the usage of novel opensource frameworks and bring together a unified pipeline for the same. Improvements in various performance metrics that are obtained from our experimental setup are presented in this work accordingly.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115510383","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 : 2022-08-26DOI: 10.1109/ASIANCON55314.2022.9909465
Shafquat Rana, Danish Mushtaq, Nawaz Ali Warsi, M. Sarwar, A. Siddiqui
The increase in demand of electricity consumption and concerns towards environmental issues has led the concept of hybrid renewable energy microgrids to meet both the needs simultaneously. Therefore, in this paper, the study and simulation of such microgrid is carried out along with consideration of the net present cost, capital expenditure, operating expenditure, annual energy cost and the emission of carbon dioxide of the system. For this purpose, the site and load profile of an institute is considered located in New Delhi. The optimization is carried out using HOMER PRO software. The most suitable system topology is considered which is able to meet the set objective function in paper. Sensitivity analysis is also performed on the proposed system. Furthermore, the proposed system configuration i.e., solar PV-battery microgrid is compared to traditional grid-only supply system.
{"title":"Grid-connected Hybrid Renewable Energy based Microgrid Optimization for Sustainable Energy Supply","authors":"Shafquat Rana, Danish Mushtaq, Nawaz Ali Warsi, M. Sarwar, A. Siddiqui","doi":"10.1109/ASIANCON55314.2022.9909465","DOIUrl":"https://doi.org/10.1109/ASIANCON55314.2022.9909465","url":null,"abstract":"The increase in demand of electricity consumption and concerns towards environmental issues has led the concept of hybrid renewable energy microgrids to meet both the needs simultaneously. Therefore, in this paper, the study and simulation of such microgrid is carried out along with consideration of the net present cost, capital expenditure, operating expenditure, annual energy cost and the emission of carbon dioxide of the system. For this purpose, the site and load profile of an institute is considered located in New Delhi. The optimization is carried out using HOMER PRO software. The most suitable system topology is considered which is able to meet the set objective function in paper. Sensitivity analysis is also performed on the proposed system. Furthermore, the proposed system configuration i.e., solar PV-battery microgrid is compared to traditional grid-only supply system.","PeriodicalId":429704,"journal":{"name":"2022 2nd Asian Conference on Innovation in Technology (ASIANCON)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116018350","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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