Digital Home Control for Dynamic Utility Pricing and Inventory
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Solution Overview
Problem
Existing residential systems lack holistic solutions for managing energy, water, and product consumption in homes, failing to adapt to variable energy costs and demand, and do not provide comprehensive monitoring and personalized recommendations for efficient resource use and inventory management.
Innovation Solution
A digital system that integrates multiple data sources, including smart meters and devices, to track and analyze consumption habits, providing personalized recommendations for energy and water usage, optimizing timing and inventory management, and allowing integration of new devices and services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If consumers manually manage consumptive tasks against variable cost models, then they can control their spending, but the complexity and time required to track and optimize becomes extremely complicated
Solution Approach 1:
The system enables automated self-management of consumptive tasks by having appliances and devices autonomously monitor their own usage, track costs against variable pricing models, and schedule operations to optimize spending. The system self-adjusts without requiring manual consumer intervention to manage the complexity of time-varying energy and water costs.
Solution Approach 2:
The system implements continuous feedback loops where consumption data from smart meters and device sensors is automatically fed back to the control system, which then adjusts scheduling and operations in real-time based on current pricing signals and usage patterns, enabling dynamic optimization without manual oversight.
2Ease of operation
If consumers switch to direct-to-consumer internet-based product procurement, then convenience and sustainability improve, but inventory management becomes more challenging with potential delays
Solution Approach 1:
The system proactively monitors product inventory levels across the household and automatically places reorder requests with internet-based suppliers before products are completely depleted. By performing the procurement action in advance based on predicted consumption rates, the system prevents stockouts and eliminates waiting time for essential household products.
Solution Approach 2:
The system replaces manual inventory tracking and product reordering with an automated digital monitoring and procurement system that uses sensors, data communication, and algorithmic decision-making to manage household product inventory, eliminating the need for manual checking and ordering while reducing delivery delays.
3Measurement precision
If fragmented monitoring solutions are used for specific resources, then specialized tracking is achieved, but holistic home management and coordinated optimization are lacking
Solution Approach 1:
The system creates a universal platform that integrates monitoring and management of multiple resources including energy, water, and household products within a single coordinated system. This multi-functional platform provides holistic home management capabilities while maintaining the specialized tracking precision of individual resources through unified data collection and analysis.
Solution Approach 2:
The system merges previously fragmented monitoring solutions into a single integrated system that combines energy management, water management, inventory tracking, and appliance control into one coordinated platform, enabling holistic optimization while preserving the measurement precision of individual resource tracking through unified sensors and data processing.
Data Source
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AI summary
A computer implemented method for generating parameters associated with at least one consumption event within a domicile, the method having the steps of identifying at least one consumption event selected from the group consisting of water usage, energy usage, and product usage, generating data by at least one input data source within the domicile, collecting the data, and, processing the data with the computer to calculate the parameters associated with the at least one consumption event.