This book analyses issues surrounding the efficient integration of demand response programmes in residential communities. It also explores the benefits and energy efficiency co-ordination corresponding to demand response service in a cooperative system. The author explains how sensors, communication technologies, computational ability, and control can be effectively combined to create a demand planification system.
Smart appliances (SAs) and other connected devices, together with smart communities (SCs)―which enable energy consumers to pursue common goals through cooperation and coordinated behaviour within the framework of the Internet of things (IoT)―have raised expectations regarding deployment of the information and communication technologies (ICTs) to encourage uptake of demand response (DR) energy efficiency programmes. DR programmes pursue a reduced carbon footprint, balanced supply and demand, and behavioural change in consumers.
The book provides sustainable evidence of ICT-supported energy management that can help consumers flexibly manage demand through the formation of sustainable SCs that maximise renewable energy use through large-scale cooperative management mechanisms. SCs cooperating securely in identifying consumption patterns can foster sustainable and efficient energy use, with the outcome of benefits for the participants and for the environment.

lgli/3031499913.pdf

lgrsnf/3031499913.pdf

Springer Nature Switzerland AG

Springer Nature, Cham, 2024

Switzerland, Switzerland

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Foreword
Preface
Author Presentation
Contents
Acronyms
1 Introduction
2 Energy Demand Management
2.1 Cooperation System Challenges
2.2 Demand Management
2.2.1 Software and Hardware Platforms
2.2.2 Communication Infrastructures and Protocols
2.2.3 Pilots and Demand Aggregation Examples
2.3 Summary
References
3 Demand Aggregation: System Architecture and Design
3.1 Cooperative Demand Scheduler
3.1.1 Demand Scheduler Architecture
3.1.2 Consumer: Household Unit
3.1.3 Utility
3.1.4 Demand Aggregator: Community Unit
3.2 Optimization Techniques
3.3 Demand Aggregation Algorithms
3.4 Summary
References
4 Evaluation of Scheduling Algorithms
4.1 Performance Evaluation
4.1.1 Evaluation Using Heuristic Techniques
4.1.2 Evaluation Strategies
4.1.3 Identifying Consumer Behaviors
4.1.4 Microgeneration Evaluation
4.2 Summary
References
5 Behavioral Analysis and Pattern Validation
5.1 ML Techniques
5.2 Demand Segmentation and Forecasting Tools
5.3 Behavioral Pattern Analysis
5.3.1 Behavior Pattern Analysis
5.3.2 Unsupervised ML Analysis
5.3.3 Supervised ML Analysis
5.3.4 Added Value of Demand Forecasting
5.3.5 Forecasting Characterization and Evaluation
5.4 Summary
References
6 Experimental Demand Scheduler Validation
6.1 Scheduler Implementation
6.1.1 Computation Costs
6.1.2 Communication Cost
6.1.3 Security Analysis
6.2 Summary
References
7 Conclusions
Appendix Questionnaire for Deploying DR Systems
Reference

The Springer Series in Sustainable Energy Policy
Erscheinungsdatum: 09.02.2024

2024-03-09