Energy Usage Computing System for Smart Device Budgeting
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Solution Overview
Problem
The cost of operating smart devices and other energy-consuming devices can vary significantly from month to month, often exceeding user budgets due to unpredictable energy usage patterns.
Innovation Solution
An energy usage computing system that receives data from sensors to determine energy usage and project estimated costs, comparing these to user budgets and performing cost-saving actions when costs exceed thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy usage is not monitored and budgeted, then device operation is simple and uninterrupted, but energy costs exceed user budgets due to unpredictable usage patterns
Solution Approach 1:
The system performs preliminary actions by projecting estimated costs before they become actual expenses. It calculates projected energy costs based on historical usage patterns and compares them to user budgets in advance, allowing the system to take preventive cost-saving actions before budget overruns occur. This proactive approach ensures budget adherence without requiring complex real-time intervention systems.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual energy usage, comparing it to projected costs and user budgets, and providing feedback through notifications and adjustments. This feedback loop allows the system to adapt to changing usage patterns and maintain budget adherence while managing complexity through automated iterative processes.
2Loss of energy
If the system automatically adjusts device operations to save costs, then energy costs are reduced, but device functionality may be compromised
Solution Approach 1:
The system applies dynamics by adjusting device operations flexibly based on projected costs and user preferences rather than implementing static rigid controls. It can dynamically modify operational parameters such as temperature setpoints, scheduling, or power consumption levels in response to changing energy prices, usage patterns, and budget constraints, thereby reducing energy costs while preserving necessary device functionality through adaptive rather than fixed control strategies.
Data Source
AI summary
An energy usage computing system includes one or more processors that are configured to receive data corresponding to energy usage of a plurality of devices within a property from a plurality of sensors. The energy usage computing system is also configured to analyze the data to determine an amount of energy used for at least one device of the plurality of devices, project an estimated cost of operation for the at least one device based at least in part on the determined amount of energy used for the at least one device, compare the estimated cost of operation for the at least one device to a threshold, and perform a cost-saving action in response to determining that the estimated cost of operation for the at least one device is above the threshold.