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.
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.
