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Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm
1Department of Computer Science and Information Management, Providence University, No. 200, Sec. 7, Taiwan Boulevard, Shalu Dist., Taichung City 43301, Taiwan. yh.lin@pu.edu.tw.
Sensors (Basel, Switzerland)
|April 28, 2018
Summary
This study proposes a smart home energy management system using constrained Particle Swarm Optimization (PSO) for appliance scheduling. It reduces peak power consumption by 13.97% while balancing cost and user comfort in Demand Response (DR) schemes.
Area of Science:
- Smart Grid Technology
- Internet of Things (IoT)
- Artificial Intelligence in Energy Management
Background:
- Smart homes leverage IoT devices for energy management within smart grids.
- Demand Response (DR) schemes aim to modify consumer energy use based on pricing signals.
- Balancing energy costs and user comfort is a key challenge in residential DR.
Purpose of the Study:
- To propose a consumer-centric load-scheduling method for residential smart homes.
- To integrate edge computing for efficient data processing in DR.
- To minimize energy costs while ensuring user comfort satisfaction.
Main Methods:
- A constrained Particle Swarm Optimization (PSO) algorithm for intelligent load scheduling.
- Utilizing a non-intrusive load-monitoring technique for appliance characteristic determination.
- Implementing edge computing for low-latency data analytics at the IoT edge.
Main Results:
- The proposed method effectively reshapes home appliance loads in response to DR signals.
- Demonstrated significant reduction in peak power consumption by 13.97%.
- Validated through a case study under real-time pricing with inclining block rates.
Conclusions:
- The consumer-centric load-scheduling method enhances DR effectiveness in smart homes.
- Edge computing integration improves responsiveness for latency-sensitive DR applications.
- The PSO-based approach successfully optimizes energy consumption cost and user comfort.
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