Context-Aware Home Energy Scheduling for Comfort and Savings
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Solution Overview
Problem
Current energy management systems for homes lack the ability to dynamically adjust energy consumption based on occupancy levels, usage patterns, and weather conditions, failing to effectively reduce overall energy use and achieve net zero energy goals, as they do not coordinate energy use among various appliances and renewable resources.
Innovation Solution
A context-aware smart home energy management system (CASHEM) that identifies contextual information within a household, selects comfort of service preferences, and generates appliance use schedules for maximum energy savings, integrating renewable energy sources and motivating energy-saving behaviors through incentives, while communicating with various energy-consuming devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If a comprehensive energy management system coordinates all appliances and renewable resources, then energy savings increase significantly, but system complexity increases
Solution Approach 1:
The system segments energy management into appliance-specific modules, each with its own controller that can be independently managed. This allows comprehensive coordination of multiple appliances while maintaining modular architecture that limits overall system complexity.
Solution Approach 2:
A centralized energy management controller acts as an intermediary between individual appliance controllers and the utility company. This mediator coordinates energy usage across all appliances, optimizes renewable energy integration, and manages demand response programs without requiring direct complex interactions between all system components.
2Productivity
If the system dynamically adjusts appliance schedules based on contextual information, then energy efficiency improves, but measurement and detection difficulty increases
Solution Approach 1:
The system continuously monitors contextual information including occupancy status, weather conditions, utility pricing signals, and actual energy consumption. This feedback is processed to dynamically adjust appliance schedules, creating a closed-loop system that improves energy efficiency while systematically managing the complexity of multiple data sources.
Solution Approach 2:
The energy management controller performs multiple functions: monitoring contextual information, optimizing appliance schedules, integrating renewable energy sources, managing demand response programs, and providing user interfaces. This multi-functional approach consolidates diverse detection and control tasks into a single coordinated system.
3Adaptability or versatility
If the system integrates renewable energy sources and storage, then energy independence increases, but device complexity increases
Solution Approach 1:
The system merges renewable energy sources (solar panels, wind turbines), energy storage devices (batteries), and appliance load management into a single integrated energy ecosystem. This consolidation allows the system to function as a unified microgrid that maximizes energy independence while managing complexity through integrated control.
4Loss of energy
If the system provides real-time monitoring and dynamic scheduling, then energy savings increase, but ease of operation decreases
Solution Approach 1:
The system automatically monitors energy consumption, detects optimization opportunities, and adjusts appliance schedules without requiring continuous user intervention. This self-service capability reduces energy waste through real-time optimization while maintaining ease of operation by eliminating the need for users to manually manage complex scheduling decisions.
Data Source
AI summary
A context-aware smart home energy management (CASHEM) system and method is disclosed. CASHEM dynamically schedules household energy use to reduce energy consumption by identifying contextual information within said household, selecting a comfort of service preference, wherein said comfort of service preference is based on different said contextual information, and extracting an appliance use schedule for maximum energy savings based on said contextual information in light of said comfort of service preferences, by executing a program instruction in a data processing apparatus. CASHEM correlates said contextual information with energy consumption levels to dynamically schedule said appliance based on an energy-saving condition and a user's comfort. Comfort of service preferences are gathered by CASHEM by monitoring occupant activity levels and use of said appliance. CASHEM can also recommend potential energy savings for a user to modify comfort of service preferences.