Automatic Thresholding for Sensor Data Gap Detection Using Statistical Approach

Najeh, Houda; Singh, Mahendra; Ploix, Stéphane; Chabir, Karim; Abdelkrim, Mohamed Naceur

doi:10.1007/978-981-32-9868-2_39

Cited by 2 publications

(2 citation statements)

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“…The performance of the overall FDI/FTC system depends on many factors such as the availability of actuators and the fact that the sensors are always in good operation mode and that all the data are available [40]. However, on the one hand, data gaps i.e., missing data are the most important sensor fault types in buildings [47]. This is a challenge.…”

Section: New Challenges For Fault-tolerant Control In Building Systemsmentioning

confidence: 99%

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

et al. 2021

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Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.

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Section: New Challenges For Fault-tolerant Control In Building Systemsmentioning

confidence: 99%

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

et al. 2021

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“…However, collecting a sufficient amount of sensor data this way to be able to meaningfully train ML algorithms is a time-consuming and cumbersome task. In addition, collected data can contain gaps [36] or incorrect samples due to sensor measurement errors, the data themselves or even the ground-truth data are not always annotated or there can be inaccuracies in labeling [37]; data are biased in some way [38], or they cannot be collected at all due to privacy regulations. Since data collection is cumbersome, alternative methods of data collection are already being explored, such as the participatory collection of data from people [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

Meiser,

Zinnikus

2024

Energies

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To achieve the energy transition, energy and energy efficiency are becoming more and more important in society. New methods, such as Artificial Intelligence (AI) and Machine Learning (ML) models, are needed to coordinate supply and demand and address the challenges of the energy transition. AI and ML are already being applied to a growing number of energy infrastructure applications, ranging from energy generation to energy forecasting and human activity recognition services. Given the rapid development of AI and ML, the importance of Trustworthy AI is growing as it takes on increasingly responsible tasks. Particularly in the energy domain, Trustworthy AI plays a decisive role in designing and implementing efficient and reliable solutions. Trustworthy AI can be considered from two perspectives, the Model-Centric AI (MCAI) and the Data-Centric AI (DCAI) approach. We focus on the DCAI approach, which relies on large amounts of data of sufficient quality. These data are becoming more and more synthetically generated. To address this trend, we introduce the concept of Synthetic Data-Centric AI (SDCAI). In this survey, we examine Trustworthy AI within a Synthetic Data-Centric AI context, focusing specifically on the role of simulation and synthetic data in enhancing the level of Trustworthy AI in the energy domain.

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Automatic Thresholding for Sensor Data Gap Detection Using Statistical Approach

Cited by 2 publications

References 15 publications

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

Model Predictive Control for Microgrid Functionalities: Review and Future Challenges

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

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