Research Topic

Data-Driven Energy Demand Side Management Techniques

About this Research Topic

Ever-increasing prevalence of advanced metering infrastructure, Internet-of-Things (IoT) sensors, distributed energy resources and automation technologies have been significantly driving energy demand-side entities to operate from passive energy consumers to energy prosumers (producers-and-consumers) that proactively participate in the grid’s operation. Further, these ubiquitous data acquisition facilities, together with recent machine learning advances, provide unprecedented opportunities to fuse multi-disciplinary knowledge to understand the energy customers’ operational environments in a fine-grained manner and develop new data-driven Demand Side Management (DSM) techniques.

This Research Topic solicits the latest and original contributions on a wide range of data-driven demand side management techniques, including cutting-edge modelling methodologies of demand-side energy entities, DSM algorithms, and innovative data-driven DSM applications in smart grid context. Works that focus on machine learning based applications in energy demand side are particularly welcome. All the submissions will go into a quick and high-quality peer-review process for fast publication.

The Research Topic invites submissions on all topics of data-driven theories, algorithms and applications for energy demand side management, including but not limited to:
• Deep neural model for demand side management,
• Customer energy consumption data-driven energy pricing,
• Non-intrusive appliance load monitoring techniques,
• Behavior learning and analysis of energy customers,
• Data-driven building/home energy management systems,
• Vehicle-to-grid, vehicle-to-community, and vehicle-to-building/home integrations,
• Peer-to-peer energy trading in local energy markets,
• Social knowledge based demand side management techniques,
• Data security and integrity issues in demand side management,
• Reinforcement learning based demand side management applications,
• Complex behavior modeling and analysis for energy prosumers,
• Psychology-driven building/home energy management systems.


Keywords: Internet-of-Things, Demand Response, Demand Side Management, Machine Learning, Smart Grid


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Ever-increasing prevalence of advanced metering infrastructure, Internet-of-Things (IoT) sensors, distributed energy resources and automation technologies have been significantly driving energy demand-side entities to operate from passive energy consumers to energy prosumers (producers-and-consumers) that proactively participate in the grid’s operation. Further, these ubiquitous data acquisition facilities, together with recent machine learning advances, provide unprecedented opportunities to fuse multi-disciplinary knowledge to understand the energy customers’ operational environments in a fine-grained manner and develop new data-driven Demand Side Management (DSM) techniques.

This Research Topic solicits the latest and original contributions on a wide range of data-driven demand side management techniques, including cutting-edge modelling methodologies of demand-side energy entities, DSM algorithms, and innovative data-driven DSM applications in smart grid context. Works that focus on machine learning based applications in energy demand side are particularly welcome. All the submissions will go into a quick and high-quality peer-review process for fast publication.

The Research Topic invites submissions on all topics of data-driven theories, algorithms and applications for energy demand side management, including but not limited to:
• Deep neural model for demand side management,
• Customer energy consumption data-driven energy pricing,
• Non-intrusive appliance load monitoring techniques,
• Behavior learning and analysis of energy customers,
• Data-driven building/home energy management systems,
• Vehicle-to-grid, vehicle-to-community, and vehicle-to-building/home integrations,
• Peer-to-peer energy trading in local energy markets,
• Social knowledge based demand side management techniques,
• Data security and integrity issues in demand side management,
• Reinforcement learning based demand side management applications,
• Complex behavior modeling and analysis for energy prosumers,
• Psychology-driven building/home energy management systems.


Keywords: Internet-of-Things, Demand Response, Demand Side Management, Machine Learning, Smart Grid


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

13 December 2020 Abstract
12 April 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

13 December 2020 Abstract
12 April 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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