Server-Based Energy Usage Optimization System
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Solution Overview
Problem
Consumers face complexity in selecting energy plans and managing energy usage due to lack of access to relevant pricing data and time to optimize energy consumption, leading to increased energy costs and inefficient use of renewable sources.
Innovation Solution
A method that determines average historical energy usage, selects optimized energy plans based on available options, and controls energy usage devices to minimize peak demand and costs, using a server to automate energy management and device settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If consumers manually analyze energy pricing data and optimize device settings, then energy cost optimization can be achieved, but consumers lack time and expertise to perform this analysis
Solution Approach 1:
The system enables self-service by automatically analyzing energy pricing data and optimizing device settings without requiring consumer intervention. The server performs comprehensive data analysis, plan comparison, and device optimization autonomously, allowing consumers to benefit from expert-level energy management while investing minimal time or expertise.
Solution Approach 2:
An intermediary server system is introduced between the consumer and the complex energy pricing data. This server acts as a mediator that automatically processes pricing information, compares energy plans, and implements optimizations, eliminating the need for consumers to directly engage with complicated data analysis while still achieving cost reduction.
2Adaptability or versatility
If multiple energy plans and supplier options are made available, then consumer choice and potential cost savings increase, but the complexity of selection and management increases
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor energy consumption patterns, pricing data, and device performance. Based on this feedback, the server automatically adjusts and optimizes energy plan selections and device settings, managing the complexity of multiple options through continuous adaptive optimization rather than requiring manual consumer decision-making.
Solution Approach 2:
The system manages complexity by dynamically changing parameters such as energy plan selections, pricing structures, and device operational settings. The server automatically adjusts these parameters based on real-time data analysis, allowing the system to adapt to multiple options and conditions without presenting the full complexity to the consumer.
3Loss of energy
If energy usage is optimized to reduce costs, then energy savings increase, but the complexity of managing and controlling energy devices increases
Solution Approach 1:
Energy devices are configured to operate autonomously based on optimization parameters set by the server. The system implements self-service by automatically adjusting device settings and operational parameters without requiring ongoing consumer management, thereby achieving cost reduction while minimizing the perceived complexity for end users.
Solution Approach 2:
The server performs preliminary optimization actions by pre-configuring device settings and energy plan parameters before consumption occurs. This advance optimization eliminates the need for real-time consumer intervention in device management, reducing both costs and the perceived complexity of ongoing management.
Data Source
AI summary
Implementations of the disclosed subject matter may provide a method includes determining, at a server, average historical usage of energy by a user based on received energy usage data. The server may determine at least one available energy usage plan from one or more energy providers based on the determined average historical usage of energy and by determining available energy rate structures. The server may determine an optimized energy usage from the one or more energy providers based on the determined at least one available energy usage plan. The method may include controlling, at the server, one or more setting of an energy usage device based on the determined optimized energy usage and a selected energy usage plan from the determined at least one energy usage plan.