AI and data driven new energy systems

W. C. Kong; Z. Y. Dong

doi:10.1049/cp.2018.1739

AI and data driven new energy systems

W. C. Kong
, Z. Y. Dong

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Energy systems today are increasingly featured by new elements with high level of uncertainty. One of most prominent sources of uncertainty is from renewable resources, which are heavily embraced to build a more sustainable energy future. To accommodate the intermittent nature of renewable resources, demand side is expected to be involved actively in future energy system operations. The complexity involved with multiple energy sources and interaction from end users has introduced high level of difficulties for accurate modelling and thus control of the system. Conventional simulation-based methods can be either computationally expensive or inaccurate due to modelling defects. With recent development in artificial intelligence, especially on deep learning technologies and other data-based methodologies, new approaches for energy system operations and energy market opportunities have appeared. In this talk, some key issues, challenges and methodologies for new energy system and market operations will be introduced. Advanced data analytics and artificial intelligence for research, and case studies with real market data will be presented as well. © 2018 Institution of Engineering and Technology. All rights reserved.

Original language	English
Title of host publication	IET Conference Publications
Publisher	Institution of Engineering and Technology
Volume	2018
ISBN (Print)	9781785617911, 9781839530043
DOIs	https://doi.org/10.1049/cp.2018.1739
Publication status	Published - 2018
Externally published	Yes
Event	11th IET International Conference on Advances in Power System Control, Operation and Management, APSCOM 2018 - Hong Kong, China Duration: 11 Nov 2018 → 15 Nov 2018

Conference

Conference	11th IET International Conference on Advances in Power System Control, Operation and Management, APSCOM 2018
Place	China
City	Hong Kong
Period	11/11/18 → 15/11/18

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Research Keywords

Data driven approaches
Deep learning
Non-intrusive load monitoring
Renewable generation forecasting
Residential load forecasting

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10.1049/cp.2018.1739

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Cite this

@inproceedings{a47694a3a8dd4c36be4e4f04bd70d3b3,

title = "AI and data driven new energy systems",

abstract = "Energy systems today are increasingly featured by new elements with high level of uncertainty. One of most prominent sources of uncertainty is from renewable resources, which are heavily embraced to build a more sustainable energy future. To accommodate the intermittent nature of renewable resources, demand side is expected to be involved actively in future energy system operations. The complexity involved with multiple energy sources and interaction from end users has introduced high level of difficulties for accurate modelling and thus control of the system. Conventional simulation-based methods can be either computationally expensive or inaccurate due to modelling defects. With recent development in artificial intelligence, especially on deep learning technologies and other data-based methodologies, new approaches for energy system operations and energy market opportunities have appeared. In this talk, some key issues, challenges and methodologies for new energy system and market operations will be introduced. Advanced data analytics and artificial intelligence for research, and case studies with real market data will be presented as well. {\textcopyright} 2018 Institution of Engineering and Technology. All rights reserved.",

keywords = "Data driven approaches, Deep learning, Non-intrusive load monitoring, Renewable generation forecasting, Residential load forecasting",

author = "Kong, \{W. C.\} and Dong, \{Z. Y.\}",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 11th IET International Conference on Advances in Power System Control, Operation and Management, APSCOM 2018 ; Conference date: 11-11-2018 Through 15-11-2018",

year = "2018",

doi = "10.1049/cp.2018.1739",

language = "English",

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volume = "2018",

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TY - GEN

T1 - AI and data driven new energy systems

AU - Kong, W. C.

AU - Dong, Z. Y.

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2018

Y1 - 2018

N2 - Energy systems today are increasingly featured by new elements with high level of uncertainty. One of most prominent sources of uncertainty is from renewable resources, which are heavily embraced to build a more sustainable energy future. To accommodate the intermittent nature of renewable resources, demand side is expected to be involved actively in future energy system operations. The complexity involved with multiple energy sources and interaction from end users has introduced high level of difficulties for accurate modelling and thus control of the system. Conventional simulation-based methods can be either computationally expensive or inaccurate due to modelling defects. With recent development in artificial intelligence, especially on deep learning technologies and other data-based methodologies, new approaches for energy system operations and energy market opportunities have appeared. In this talk, some key issues, challenges and methodologies for new energy system and market operations will be introduced. Advanced data analytics and artificial intelligence for research, and case studies with real market data will be presented as well. © 2018 Institution of Engineering and Technology. All rights reserved.

AB - Energy systems today are increasingly featured by new elements with high level of uncertainty. One of most prominent sources of uncertainty is from renewable resources, which are heavily embraced to build a more sustainable energy future. To accommodate the intermittent nature of renewable resources, demand side is expected to be involved actively in future energy system operations. The complexity involved with multiple energy sources and interaction from end users has introduced high level of difficulties for accurate modelling and thus control of the system. Conventional simulation-based methods can be either computationally expensive or inaccurate due to modelling defects. With recent development in artificial intelligence, especially on deep learning technologies and other data-based methodologies, new approaches for energy system operations and energy market opportunities have appeared. In this talk, some key issues, challenges and methodologies for new energy system and market operations will be introduced. Advanced data analytics and artificial intelligence for research, and case studies with real market data will be presented as well. © 2018 Institution of Engineering and Technology. All rights reserved.

KW - Data driven approaches

KW - Deep learning

KW - Non-intrusive load monitoring

KW - Renewable generation forecasting

KW - Residential load forecasting

UR - https://www.scopus.com/pages/publications/85081697093

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85081697093&origin=recordpage

U2 - 10.1049/cp.2018.1739

DO - 10.1049/cp.2018.1739

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781785617911

SN - 9781839530043

VL - 2018

BT - IET Conference Publications

PB - Institution of Engineering and Technology

T2 - 11th IET International Conference on Advances in Power System Control, Operation and Management, APSCOM 2018

Y2 - 11 November 2018 through 15 November 2018

ER -

AI and data driven new energy systems

Abstract

Conference

Bibliographical note

UN SDGs

Research Keywords

Access Output

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