AI-Solutions for Demand Side Management (DSM)

Project: Applied Research

Project Details

Abstract

The Demand-Side Management (DSM) Project is set to revolutionize energy efficiency and conservation in residential communities. At its core is "PowerShift Connect," an innovative platform that utilizes predictive analytics and decision-support tools, alongside a community-based energy exchange, to optimize energy consumption. By integrating both hardware and software solutions, the project actively monitors and simulates energy usage patterns in homes, while incorporating behavioral insights to design effective nudges that encourage energy-saving behaviors. To promote compliance and engagement, the DSM Project develops customized policies and frameworks that align with regulatory standards. This dedication to regulatory alignment ensures that participants are informed and motivated to adhere to the outlined guidelines. Moreover, the project evaluates the impact of Distributed Energy Resources (DER) on grid stability, using real-time data, AI-driven forecasting, and optimization models to enhance energy distribution strategies. This holistic approach aims to transform residential energy systems, making them more efficient and resilient. By empowering communities to take an active role in energy management through tools and resources, the DSM Project fosters a sustainable energy future. Through innovative technology and community engagement, "Powershift" Connect" is poised to lead the way in driving significant energy savings and optimizing resource utilization in homes.

Submitting Institute Name

Hamad Bin Khalifa University (HBKU)
Sponsor's Award NumberQEE314-ELEGE-0125-DSM-010
Proposal IDQEERI-CORE-000010
StatusActive
Effective start/end date1/01/251/01/28

Primary Theme

  • Sustainability

Primary Subtheme

  • SU - Sustainable Energy

Secondary Theme

  • Sustainability

Secondary Subtheme

  • SU - Sustainable / Circular Economy

Keywords

  • Demand Side Management
  • Distributed Energy Resources
  • AI-driven Forecast

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