Digitalization and decentralization driving transactive energy Internet: Key technologies and infrastructures

Wu, Ying; Wu, Yanpeng; Guerrero, Josep M.; Vasquez, Juan C.

doi:10.1016/j.ijepes.2020.106593

Cited by 99 publications

(48 citation statements)

References 41 publications

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“…Data sampling values above 1 kHz are considered high, increasing the complexity of data storage, transmission, and processing compared to low-speed sampling. Therefore, high-speed sampling is often considered far from being a practical approach for large-scale applications [25]. It is possible to measure both time-dependent and/or frequency-based features.…”

Section: Intrusive Load Monitoringmentioning

confidence: 99%

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IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

et al. 2021

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Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Three different classifier models are tested using real data from the UK-DALE dataset: feed-forward neural network (FFNN), long short-term memory (LSTM), and support vector machine (SVM). The developed activities of daily living (ADL) algorithm maps each ADL to a set of criteria depending on the appliance used. The features are extracted according to the consumption in Watt-hours and the times where they are switched on. In the FFNN and the LSTM networks, the accuracy is above 0.9 while around 0.8 for the SVM network. Other experiments are performed to evaluate the classifier model using a new test set. A sensitivity analysis is also carried out to study the impact of the group size on the classifier accuracy.

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Section: Intrusive Load Monitoringmentioning

confidence: 99%

“…deployed devices. Functional requirements, including data management, data storage, big data analysis, realtime data analysis, and deep data analysis with AI should be considered [25].  At the top of the architecture lies the application layer.…”

Section: Intrusive Load Monitoringmentioning

confidence: 99%

IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

et al. 2021

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“…Figure 2 is inspired by [ 59 ] and has been adapted by the authors to discuss the specific case of the energy informatics domain. As shown in the figure, blockchain for data security [ 60 ], artificial intelligence (AI) [ 61 ], 5G [ 62 ], and transactive energy [ 63 ] are in the technology trigger phase where some proof-of-concept stories and media interest leads to popularity. The plateau will be reached in 2–5 years for the blue and in 5–10 years for the black vertical arrows.…”

Section: Industrial Innovation Driven By Energy Informaticsmentioning

confidence: 99%

Educating the energy informatics specialist: opportunities and challenges in light of research and industrial trends

et al. 2021

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Contemporary energy research is becoming more interdisciplinary through the involvement of technical, economic, and social aspects that must be addressed simultaneously. Within such interdisciplinary energy research, the novel domain of energy informatics plays an important role, as it involves different disciplines addressing the socio-techno-economic challenges of sustainable energy and power systems in a holistic manner. The objective of this paper is to draw an overview of the novel domain of energy informatics by addressing the educational opportunities as well as related challenges in light of current trends and the future direction of research and industrial innovation. In this study we discuss the energy informatics domain in a way that goes beyond a purely scientific research perspective. This paper widens the analyses by including reflections on current and future didactic approaches with industrial innovation and research as a background. This paper provides key recommendations for the content of a foundational introductory energy informatics course, as well as suggestions on distinguishing features to be addressed through more specialized courses in the field. The importance of this work is based on the need for better guidelines for a more appropriate education of a new generation of experts who can take on the novel interdisciplinary challenges present in future integrated, sustainable energy systems. Article highlights Didactic approaches in the energy informatics domain are discussed based on research and industrial trends. Research trends and industrial innovation driven by energy informatics are investigated. A fundamental framework for an energy informatics course is defined together with specialized distinguishing features.

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“…The construction of the energy Internet needs to be supported by breakthroughs in key technologies such as energy production, transformation, transmission, storage and access [6]. In terms of energy production, distributed photovoltaic, wind power and other new energy technologies will realize low-carbon and clean energy Internet.…”

Section: Multi Technologymentioning

confidence: 99%

Research on Influence of Energy Internet on Organizational Form of Power Grid Enterprise

Zhang¹,

Zhao²

2021

Proceedings of the 6th International Conference on Financial Innovation and Economic Development (ICFIED 2021)

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The Energy Internet is deeply integrated with energy systems and emerging technologies such as the "Cloud Computing, Big Data Technologies, the Internet of Things, Mobile Internet, Artificial Intelligence", which accelerates the transformation of the traditional energy system driven by fossil energy into a new energy system that is clean, lowcarbon, efficient and safe. This also brings challenges to the traditional organizational structure of power grid enterprise. By analyzing the characteristics of the Energy Internet, the general law of evolution of the enterprise form, and the developing history of typical Internet enterprises, this paper proposes the direction of business evolution of the Energy Internet, and the direction of the organizational structure optimization for the power grid enterprise, so as to provide some support for the transformation of enterprise's organizational form in the future.

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Digitalization and decentralization driving transactive energy Internet: Key technologies and infrastructures

Cited by 99 publications

References 41 publications

IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes

Educating the energy informatics specialist: opportunities and challenges in light of research and industrial trends

Research on Influence of Energy Internet on Organizational Form of Power Grid Enterprise

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