Artificial Intelligence Enabled Computational Methods For Smart Grid Forecast And Dispatch


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Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch


Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch

Author: Yuanzheng Li

language: en

Publisher: Springer Nature

Release Date: 2023-05-05


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With the increasing penetration of renewable energy and distributed energy resources, smart grid is facing great challenges, which could be divided into two categories. On the one hand, the endogenous uncertainties of renewable energy and electricity load lead to great difficulties in smart grid forecast. On the other hand, massive electric devices as well as their complex constraint relationships bring about significant difficulties in smart grid dispatch. Owe to the rapid development of artificial intelligence in recent years, several artificial intelligence enabled computational methods have been successfully applied in the smart grid and achieved good performances. Therefore, this book is concerned with the research on the key issues of artificial intelligence enabled computational methods for smart grid forecast and dispatch, which consist of three main parts. (1) Introduction for smart grid forecast and dispatch, in inclusion of reviewing previous contribution of various research methods as well as their drawbacks to analyze characteristics of smart grid forecast and dispatch. (2) Artificial intelligence enabled computational methods for smart grid forecast problems, which are devoted to present the recent approaches of deep learning and machine learning as well as their successful applications in smart grid forecast. (3) Artificial intelligence enabled computational methods for smart grid dispatch problems, consisting of edge-cutting intelligent decision-making approaches, which help determine the optimal solution of smart grid dispatch. The book is useful for university researchers, engineers, and graduate students in electrical engineering and computer science who wish to learn the core principles, methods, algorithms, and applications of artificial intelligence enabled computational methods.

Blockchain Applications for the Energy and Utilities Industry


Blockchain Applications for the Energy and Utilities Industry

Author: Alangari, Someah

language: en

Publisher: IGI Global

Release Date: 2025-05-08


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Blockchain technology revolutionizes various industries and communities, including the energy and utilities industry. Its transparency and security make it a reliable system for strengthening digital systems and data. In the energy and utilities industry, blockchain can ensure efficient grid management, secure smart metering, and secure transactions between accounts, reducing the change of failure and improving operational reliability. As a result, blockchain should be utilized as a potential solution for data integrity, mitigating threats, and protecting energy infrastructures. Furthermore, it has implications for creating a more sustainable and inclusive environment. Blockchain Applications for the Energy and Utilities Industry has a far-reaching impact, fostering knowledge sharing, collaboration, and the advancement of blockchain technology across the energy and utilities industry. It develops informed policies and frameworks for the technology's adoption and governance. Covering topics such as energy financing, disaster response, and secure communication, this book is an excellent resource for energy and utilities professionals, software engineers, technology leaders, policymakers, government officials, professionals, researchers, scholars, academicians, and more.

Data Science


Data Science

Author: Chengzhong Xu

language: en

Publisher: Springer Nature

Release Date: 2024-10-30


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This three-volume set CCIS 2213-2215 constitutes the refereed proceedings of the 10th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2024, held in Macau, China, during September 27–30, 2024. The 74 full papers and 3 short papers presented in these three volumes were carefully reviewed and selected from 249 submissions. The papers are organized in the following topical sections: Part I: Novel methods or tools used in big data and its applications; applications of data science. Part II: Education research, methods and materials for data science and engine; data security and privacy; big data mining and knowledge management. Part III: Infrastructure for data science; social media and recommendation system; multimedia data management and analysis.