Time–Frequency Mask Estimation Based on Deep Neural Network for Flexible Load Disaggregation in Buildings

Song, Junho; Lee, Yong-Gu; Hwang, Euiseok

doi:10.1109/tsg.2021.3066547

Cited by 28 publications

(12 citation statements)

References 23 publications

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“…Liang and Ma presented a non-homogeneous Factorial Hidden Markov Model (MN-FHMM) to dis-aggregate the HVAC load profile and to learn a residence HVAC patterns [23]. Song et al proposed a time-frequency masking technique paired with a deep learning model to identify the HVAC load [26]. Both of these studies employ complex probability and frequency domain signal analysis based calculations, which suffer from longer computational runtimes when im-plemented as computer programs; in addition, they were presented only conceptually, without emphasis on reproducibility.…”

Section: Novel Hvac Load Separation Methods Using Lstm Modelmentioning

confidence: 99%

Artificial Intelligence Method for the Forecast and Separation of Total and HVAC Loads With Application to Energy Management of Smart and NZE Homes

et al. 2021

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Section: Novel Hvac Load Separation Methods Using Lstm Modelmentioning

confidence: 99%

Artificial Intelligence Method for the Forecast and Separation of Total and HVAC Loads With Application to Energy Management of Smart and NZE Homes

et al. 2021

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“…nary buildings to smart buildings include 1) IoT for seamless integration and processing of building environmental parameters such as temperature, noise, humidity, electricity and water flow, human activity information, and estimation of energy usage [48]; 2) BMS for controlling building operation according to human needs and activities [167]; 3) Flexible loads for scheduling and administering of household activities [168]; 4) big data management for analysis and forecasting [169]; 5) artificial intelligence for automatic, well-informed, and real-time decision making [170]; and 6) distributed energy resources for increasing use and management of green energy [171]. A graphical illustration of green buildings is shown in Fig.…”

Section: ) Green Buildingsmentioning

confidence: 99%

A Survey of Cyber-Physical Systems From a Game-Theoretic Perspective

et al. 2023

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With the emergence of the Internet-of-Things (IoT), artificial intelligence, and communication technologies, cyber-physical systems (CPS) have revolutionized the engineering paradigm with profound applications in many aspects of society including homes, energy, agriculture, health-care, transportation, business, and manufacturing. A CPS uses suitable computational techniques such as game theory to enable different entities to interact with one another for taking necessary actions to obtain selected objectives. Recent literature on CPS has extensively used game theory to approach a variety of technical challenges. In order to make these contributions more accessible to a broader audience, there is a need for studies that can provide readers with a comprehensive understanding of different types of CPS and their attributes, then clearly outline why game theory is relevant for modeling different aspects of CPS, and also discuss how game theory has been used in relevant literature to date. This paper bridges this gap by 1) providing a general discussion of different types of CPS and their characteristics; 2) giving an overview of different types of game-theoretic approaches; 3) explaining why game theory is appropriate for modeling different types of CPS; and 4) finally, studying how game theory has been used in different CPS types to address their challenges. Further, we also identify some key research challenges for future investigation where game theory could be applied as a potential solution.

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“…2) The four electrical appliances of each group are numbered from 1 to 4. The states 𝑆 𝑡 𝑖 of the four electrical appliances in each group form a 4-bit binary number that is converted into decimal numbers with a range of [0,15]. If the number of electrical appliances in the last group is less than 4, the state range of the last group is smaller than [0,15].…”

Section: ) Appliance Switch On or Off State Combiningmentioning

confidence: 99%

“…However, at present, most of the domestic electricity meters can only measure load power or energy consumption with low-frequency. To obtain high-frequency signals, the existing widely used smart electricity meters need to be transformed, resulting in a certain additional economic cost, which hinders their large-scale application and promotion [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Novel Non-intrusive Load Monitoring Method Using Low Frequency Signal

Yuan

Jin

Wang

2023

Preprint

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Non-intrusive load monitoring(NILM) can identify the appliance categories, using time, work duration and the work states through data collected from a smart meter. With NILM technique,people can conduct the power supply and power consumption scientifically and reasonably so as to partially or completely solve the problem of imbalance between power supply and demand. The low-frequency non-invasive load state identification algorithm can identify the load using low-frequency signal without additional hardware facilities, so it has broad development prospects. However, it is difficult to improve the accuracy of low-frequency load identification because there is little information available. In this paper, a low-frequency non-invasive load state identification algorithm combining electrical switch state classification LSTM algorithm and maximum likelihood probability algorithm based on recursive reasoning is proposed. The load identification is divided into two steps, which reduces the overall difficulty and improves the accuracy of state identification. The experimental results on Ampsd2 data set show that the proposed algorithm can achieve better results than other NILM algorithms.

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Time–Frequency Mask Estimation Based on Deep Neural Network for Flexible Load Disaggregation in Buildings

Cited by 28 publications

References 23 publications

Artificial Intelligence Method for the Forecast and Separation of Total and HVAC Loads With Application to Energy Management of Smart and NZE Homes

Artificial Intelligence Method for the Forecast and Separation of Total and HVAC Loads With Application to Energy Management of Smart and NZE Homes

A Survey of Cyber-Physical Systems From a Game-Theoretic Perspective

A Novel Non-intrusive Load Monitoring Method Using Low Frequency Signal

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