Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers最新文献
The Cognitive Load Monitoring Challenge organized in the UbiTtention 2020 workshop tasked the research community with the problem of inferring a user's cognitive load from physiological measurements recorded by a low-cost wearable. This is challenging due to the subjective nature of these physiological characteristics: In contrast to related problems involving objective measurements of physical phenomena (e.g., Activity Recognition from smartphone sensors), subjects' physiological response patterns under cognitive load may be highly individual, i.e., expose significant inter-subject variance. However, models trained on datasets compiled in laboratory settings should also deliver accurate classifications when applied to measurements from novel subjects. In this work, we study the applicability of established Deep Learning models for time series classification on this challenging problem. We examine different kinds of data normalization and investigate a variant of data augmentation.
{"title":"Deep learning for cognitive load monitoring: a comparative evaluation","authors":"Andrea Salfinger","doi":"10.1145/3410530.3414433","DOIUrl":"https://doi.org/10.1145/3410530.3414433","url":null,"abstract":"The Cognitive Load Monitoring Challenge organized in the UbiTtention 2020 workshop tasked the research community with the problem of inferring a user's cognitive load from physiological measurements recorded by a low-cost wearable. This is challenging due to the subjective nature of these physiological characteristics: In contrast to related problems involving objective measurements of physical phenomena (e.g., Activity Recognition from smartphone sensors), subjects' physiological response patterns under cognitive load may be highly individual, i.e., expose significant inter-subject variance. However, models trained on datasets compiled in laboratory settings should also deliver accurate classifications when applied to measurements from novel subjects. In this work, we study the applicability of established Deep Learning models for time series classification on this challenging problem. We examine different kinds of data normalization and investigate a variant of data augmentation.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87150318","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}
In this work, we examine the suitability of automatic facial expression recognition to be used for satisfaction analysis in an Empathic Building environment. We use machine learning based facial expression recognition on the working stations to integrate an online satisfaction index into Empathic Building platform. To analyze the suitability of facial expression recognition to reflect longer-term satisfaction, we examine the changes and trends in the happiness curves of our test users. We also correlate the happiness curve with temperature, humidity, and light intensity of the test users' local city (Tampere Finland). The results indicate that the proposed analysis indeed shows some trends that may be used for long-term satisfaction analysis in different kinds of intelligent buildings.
{"title":"Facial expression based satisfaction index for empathic buildings","authors":"Fahad Sohrab, Jenni Raitoharju, M. Gabbouj","doi":"10.1145/3410530.3414443","DOIUrl":"https://doi.org/10.1145/3410530.3414443","url":null,"abstract":"In this work, we examine the suitability of automatic facial expression recognition to be used for satisfaction analysis in an Empathic Building environment. We use machine learning based facial expression recognition on the working stations to integrate an online satisfaction index into Empathic Building platform. To analyze the suitability of facial expression recognition to reflect longer-term satisfaction, we examine the changes and trends in the happiness curves of our test users. We also correlate the happiness curve with temperature, humidity, and light intensity of the test users' local city (Tampere Finland). The results indicate that the proposed analysis indeed shows some trends that may be used for long-term satisfaction analysis in different kinds of intelligent buildings.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"103 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87736118","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}
Tianyun Liu, Li Kou, Le Yang, Wenhui Fan, Cheng Wu
The learning-based methods have been widely applied to design a fault diagnosis model for rolling element bearing. However, the mainstream methods can only deal with the large training dataset, which is always violated in practical application. In this paper, we propose a physical knowledge-based hierarchical extreme learning machine(H-ELM) approach to adapt the problem of fault diagnosis for bearing with the small and imbalanced dataset. First, the proposed method uses the simple feature extraction algorithm to build a knowledge base for sample selection from the historical database, and the given training dataset is augmented with knowledge base. Second, a modified H-ELM algorithm is developed to identify fault location and recognize fault severity ranking based on the augmented dataset. Third, we design a self-optimizing module to optimize the sample selection and improve the performance of the H-ELM network. To evaluate the effectiveness of the proposed approach, the H-ELM without knowledge base and data augmentation-based support vector machine(SVM), back propagation neuron networks(BPNN) and deep belief networks(DBN) are tested in the numerical experiments to present a comprehensive comparison. The experimental results demonstrate that our approach outperforms in accuracy than other counterparts when dealing with the small and imbalanced datasets.
{"title":"A physical knowledge-based extreme learning machine approach to fault diagnosis of rolling element bearing from small datasets","authors":"Tianyun Liu, Li Kou, Le Yang, Wenhui Fan, Cheng Wu","doi":"10.1145/3410530.3414592","DOIUrl":"https://doi.org/10.1145/3410530.3414592","url":null,"abstract":"The learning-based methods have been widely applied to design a fault diagnosis model for rolling element bearing. However, the mainstream methods can only deal with the large training dataset, which is always violated in practical application. In this paper, we propose a physical knowledge-based hierarchical extreme learning machine(H-ELM) approach to adapt the problem of fault diagnosis for bearing with the small and imbalanced dataset. First, the proposed method uses the simple feature extraction algorithm to build a knowledge base for sample selection from the historical database, and the given training dataset is augmented with knowledge base. Second, a modified H-ELM algorithm is developed to identify fault location and recognize fault severity ranking based on the augmented dataset. Third, we design a self-optimizing module to optimize the sample selection and improve the performance of the H-ELM network. To evaluate the effectiveness of the proposed approach, the H-ELM without knowledge base and data augmentation-based support vector machine(SVM), back propagation neuron networks(BPNN) and deep belief networks(DBN) are tested in the numerical experiments to present a comprehensive comparison. The experimental results demonstrate that our approach outperforms in accuracy than other counterparts when dealing with the small and imbalanced datasets.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"2873 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86511062","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}
Promit Basak, Shahamat Mustavi Tasin, Malisha Islam Tapotee, Md. Mamun Sheikh, A. Sakib, Sriman Bidhan Baray, M. Ahad
Human activity recognition has important applications in healthcare, human-computer interactions and other arenas. The direct interaction between the nurse and patient can play a pivotal role in healthcare. Recognizing various activities of nurses can improve healthcare in many ways. However, it is a very daunting task due to the complexities of the activities. "The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data'' provides sensor-based accelerometer data to predict 12 activities conducted by the nurses in both the lab and real-life settings. The main difficulty of this dataset is to process the raw data because of a high imbalance among different classes. Besides, all activities have not been performed by all subjects. Our team, 'Team Apophis' has processed the data by filtering noise, applying windowing technique on time and frequency domain to extract various features from lab and field data distinctly. After merging lab and field data, the 10-fold cross-validation technique has been applied to find out the model of best performance. We have obtained a promising accuracy of 65% with an F1 score of 40% on this challenging dataset by using the Random Forest classifier.
{"title":"Complex nurse care activity recognition using statistical features","authors":"Promit Basak, Shahamat Mustavi Tasin, Malisha Islam Tapotee, Md. Mamun Sheikh, A. Sakib, Sriman Bidhan Baray, M. Ahad","doi":"10.1145/3410530.3414338","DOIUrl":"https://doi.org/10.1145/3410530.3414338","url":null,"abstract":"Human activity recognition has important applications in healthcare, human-computer interactions and other arenas. The direct interaction between the nurse and patient can play a pivotal role in healthcare. Recognizing various activities of nurses can improve healthcare in many ways. However, it is a very daunting task due to the complexities of the activities. \"The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data'' provides sensor-based accelerometer data to predict 12 activities conducted by the nurses in both the lab and real-life settings. The main difficulty of this dataset is to process the raw data because of a high imbalance among different classes. Besides, all activities have not been performed by all subjects. Our team, 'Team Apophis' has processed the data by filtering noise, applying windowing technique on time and frequency domain to extract various features from lab and field data distinctly. After merging lab and field data, the 10-fold cross-validation technique has been applied to find out the model of best performance. We have obtained a promising accuracy of 65% with an F1 score of 40% on this challenging dataset by using the Random Forest classifier.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85157413","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}
Research has shown that turn-by-turn navigation guidance has made users overly reliant on such guidance, impairing their independent wayfinding ability. This paper compares the impacts of two new types of navigation guidance - reference-based and orientation-based - on their users' ability to independently navigate to the same destinations, both as compared to each other, and as compared to two types of traditional turn-by-turn guidance, i.e., map-based and augmented-reality (AR) based. The results of our within-subjects experiment indicate that, while the use of reference-based guidance led to users taking more time to navigate when first receiving it, it boosted their subsequent ability to independently navigate to the same destination in less time, via more efficient routes, and with less assistance-seeking from their phones than either map-based or AR-based turn-by-turn navigation guidance did.
{"title":"Comparing the effects of reference-based, orientation-based, and turn-by-turn navigation guidance on users' independent navigation","authors":"Ting-Yu Kuo, Hung-Kuo Chu, Yung-Ju Chang","doi":"10.1145/3410530.3414424","DOIUrl":"https://doi.org/10.1145/3410530.3414424","url":null,"abstract":"Research has shown that turn-by-turn navigation guidance has made users overly reliant on such guidance, impairing their independent wayfinding ability. This paper compares the impacts of two new types of navigation guidance - reference-based and orientation-based - on their users' ability to independently navigate to the same destinations, both as compared to each other, and as compared to two types of traditional turn-by-turn guidance, i.e., map-based and augmented-reality (AR) based. The results of our within-subjects experiment indicate that, while the use of reference-based guidance led to users taking more time to navigate when first receiving it, it boosted their subsequent ability to independently navigate to the same destination in less time, via more efficient routes, and with less assistance-seeking from their phones than either map-based or AR-based turn-by-turn navigation guidance did.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85207965","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}
Lanqing Yang, Honglu Li, Zhaoxi Chen, Xiaoyu Ji, Yi-Chao Chen, Guangtao Xue, Chuang-Wen You
Recognizing the working appliances is of great importance for smart environment to provide services including energy conservation, user activity recognition, fire hazard prevention, etc. There have been many methods proposed to recognize appliances by analyzing the power voltage, current, electromagnetic emissions, vibration, light, and sound from appliances. Among these methods, measuring the power voltage and current requires installing intrusive sensors to each appliance. Measuring the electromagnetic emissions and vibration requires sensors to be attached or close (e.g., < 15cm) to the appliances. Methods relying on light are not universally applicable since only part of appliances generate light. Similarly, methods using sound relying on the sound from motor vibration or mechanical collision so are not applicable for many appliances. As a result, existing methods for appliance fingerprinting are intrusive, have high deployment cost, or only work for part of appliances. In this work, we proposed to use the inaudible high-frequency sound generated by the switching-mode power supply (SMPS) of the appliances as fingerprints to recognize appliances. Since SMPS is widely adopted in home appliances, the proposed method can work for most appliances. Our preliminary experiments on 18 household appliances (where 10 are of the same models) showed that the recognition accuracy achieves 97.6%.
{"title":"Appliance fingerprinting using sound from power supply","authors":"Lanqing Yang, Honglu Li, Zhaoxi Chen, Xiaoyu Ji, Yi-Chao Chen, Guangtao Xue, Chuang-Wen You","doi":"10.1145/3410530.3414385","DOIUrl":"https://doi.org/10.1145/3410530.3414385","url":null,"abstract":"Recognizing the working appliances is of great importance for smart environment to provide services including energy conservation, user activity recognition, fire hazard prevention, etc. There have been many methods proposed to recognize appliances by analyzing the power voltage, current, electromagnetic emissions, vibration, light, and sound from appliances. Among these methods, measuring the power voltage and current requires installing intrusive sensors to each appliance. Measuring the electromagnetic emissions and vibration requires sensors to be attached or close (e.g., < 15cm) to the appliances. Methods relying on light are not universally applicable since only part of appliances generate light. Similarly, methods using sound relying on the sound from motor vibration or mechanical collision so are not applicable for many appliances. As a result, existing methods for appliance fingerprinting are intrusive, have high deployment cost, or only work for part of appliances. In this work, we proposed to use the inaudible high-frequency sound generated by the switching-mode power supply (SMPS) of the appliances as fingerprints to recognize appliances. Since SMPS is widely adopted in home appliances, the proposed method can work for most appliances. Our preliminary experiments on 18 household appliances (where 10 are of the same models) showed that the recognition accuracy achieves 97.6%.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87639396","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}
Carolin Lübbe, Björn Friedrich, Sebastian J. F. Fudickar, S. Hellmers, A. Hein
The The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data addresses the important issue about care and the need for assistance systems in the nursing profession like automatic documentation systems. Data of 12 different care activities were recorded with an accelerometer attached to the right arm of the nurses. Both, laboratory and field data were taken into account. The task was to classify each activity based on the accelerometer data. We participated as team Gudetama in the challenge. We trained a Random Forest classifier and achieved an accuracy of 61.11% on our internal test set.
{"title":"Feature based random forest nurse care activity recognition using accelerometer data","authors":"Carolin Lübbe, Björn Friedrich, Sebastian J. F. Fudickar, S. Hellmers, A. Hein","doi":"10.1145/3410530.3414340","DOIUrl":"https://doi.org/10.1145/3410530.3414340","url":null,"abstract":"The The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data addresses the important issue about care and the need for assistance systems in the nursing profession like automatic documentation systems. Data of 12 different care activities were recorded with an accelerometer attached to the right arm of the nurses. Both, laboratory and field data were taken into account. The task was to classify each activity based on the accelerometer data. We participated as team Gudetama in the challenge. We trained a Random Forest classifier and achieved an accuracy of 61.11% on our internal test set.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90779383","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}
This paper describes our submission as Team-Petrichor to the competition that was organized by the SHL recognition challenge dataset authors. We compared multiple machine learning approach for classifying eight different activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). The first step was feature engineering, a wide set of statistical domain features were computed and their quality was evaluated. Finally, the appropriate machine learning model was chosen. The recognition result for the testing dataset will be presented in the summary paper of the SHL recognition challenge.
{"title":"Where are you?: human activity recognition with smartphone sensor data","authors":"Gulustan Dogan, Iremnaz Cay, Sinem Sena Ertas, Seref Recep Keskin, Nouran Alotaibi, Elif Sahin","doi":"10.1145/3410530.3414354","DOIUrl":"https://doi.org/10.1145/3410530.3414354","url":null,"abstract":"This paper describes our submission as Team-Petrichor to the competition that was organized by the SHL recognition challenge dataset authors. We compared multiple machine learning approach for classifying eight different activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). The first step was feature engineering, a wide set of statistical domain features were computed and their quality was evaluated. Finally, the appropriate machine learning model was chosen. The recognition result for the testing dataset will be presented in the summary paper of the SHL recognition challenge.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89758029","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}
One of the biggest challenges of activity data collection is the unavoidability of relying on users and keep them engaged to provide labels consistently. Recent breakthroughs in mobile platforms have proven effective in bringing deep neural networks powered intelligence into mobile devices. In this study, we propose on-device personalization using fine-tuning convolutional neural networks as a mechanism in optimizing human effort in data labeling. First, we transfer the knowledge gained by on-cloud pre-training based on crowdsourced data to mobile devices. Second, we incrementally fine-tune a personalized model on every individual device using its locally accumulated input. Then, we utilize estimated activities customized according to the on-device model inference as feedback to motivate participants to improve data labeling. We conducted a verification study and gathered activity labels with smartphone sensors. Our preliminary evaluation results indicate that the proposed method outperformed the baseline method by approximately 8% regarding accuracy recognition.
{"title":"Improving activity data collection with on-device personalization using fine-tuning","authors":"Nattaya Mairittha, Tittaya Mairittha, Sozo Inoue","doi":"10.1145/3410530.3414370","DOIUrl":"https://doi.org/10.1145/3410530.3414370","url":null,"abstract":"One of the biggest challenges of activity data collection is the unavoidability of relying on users and keep them engaged to provide labels consistently. Recent breakthroughs in mobile platforms have proven effective in bringing deep neural networks powered intelligence into mobile devices. In this study, we propose on-device personalization using fine-tuning convolutional neural networks as a mechanism in optimizing human effort in data labeling. First, we transfer the knowledge gained by on-cloud pre-training based on crowdsourced data to mobile devices. Second, we incrementally fine-tune a personalized model on every individual device using its locally accumulated input. Then, we utilize estimated activities customized according to the on-device model inference as feedback to motivate participants to improve data labeling. We conducted a verification study and gathered activity labels with smartphone sensors. Our preliminary evaluation results indicate that the proposed method outperformed the baseline method by approximately 8% regarding accuracy recognition.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88509283","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}
Pengfei Zhao, C. Gu, Zhidong Cao, Yue Xiang, Xiaohe Yan, Da Huo
This paper proposes an innovative two-stage data-driven optimization framework for a multi-energy system. Enormous energy conversion technologies are incorporated in the system to enhance the overall energy utilization efficiency, i.e., combined heat and power, power-to-gas, gas furnace, and ground source heat pump. Furthermore, a demand response program is adopted for stimulating the load shift of customers. Accordingly, both the economic performance and system reliability can be improved. The endogenous solar generation brings about high uncertainty and variability, which affects the decision making of the system operator. Therefore, a two-stage data-driven distributionally robust optimization (TSDRO) method is utilized to capture the uncertainty. A tractable semidefinite programming reformulation is obtained based on the duality theory. Case studies are implemented to demonstrate the effectiveness of applying the TSDRO on energy management.
{"title":"A two-stage data-driven multi-energy management considering demand response","authors":"Pengfei Zhao, C. Gu, Zhidong Cao, Yue Xiang, Xiaohe Yan, Da Huo","doi":"10.1145/3410530.3414587","DOIUrl":"https://doi.org/10.1145/3410530.3414587","url":null,"abstract":"This paper proposes an innovative two-stage data-driven optimization framework for a multi-energy system. Enormous energy conversion technologies are incorporated in the system to enhance the overall energy utilization efficiency, i.e., combined heat and power, power-to-gas, gas furnace, and ground source heat pump. Furthermore, a demand response program is adopted for stimulating the load shift of customers. Accordingly, both the economic performance and system reliability can be improved. The endogenous solar generation brings about high uncertainty and variability, which affects the decision making of the system operator. Therefore, a two-stage data-driven distributionally robust optimization (TSDRO) method is utilized to capture the uncertainty. A tractable semidefinite programming reformulation is obtained based on the duality theory. Case studies are implemented to demonstrate the effectiveness of applying the TSDRO on energy management.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89239406","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}
Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers
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