Server-Based Energy Usage Optimization for Smart Devices
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Solution Overview
Problem
Consumers face complexity in selecting energy plans and optimizing energy usage due to lack of access to relevant pricing data and time to analyze it, leading to increased energy consumption and costs, especially with the use of smart devices which can unintentionally increase energy usage.
Innovation Solution
A method that determines average historical energy usage, selects optimized energy plans based on available options, and controls energy usage devices to reduce consumption during peak times, using a server to manage energy supply and device settings for improved efficiency and cost reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If consumers manually analyze energy pricing data and optimize device settings, then energy cost reduction is achieved, but time consumption and complexity increase
Solution Approach 1:
The system enables self-service by automatically analyzing energy pricing data, comparing plans, and optimizing device settings without requiring consumer intervention. The server autonomously processes pricing information from multiple suppliers, determines optimal energy plans, and adjusts smart device configurations to minimize energy costs while maintaining consumer comfort preferences.
Solution Approach 2:
An intermediary server system is introduced between consumers and energy suppliers to handle the complexity of data analysis. The server acts as a mediator that collects pricing data from multiple suppliers, processes it according to consumer preferences, and automatically implements optimal energy plans, thereby saving consumer time and effort.
2Adaptability or versatility
If consumers access multiple supplier options and analyze pricing data, then better energy plan selection is achieved, but system complexity increases
Solution Approach 1:
The system extracts and centralizes the complex task of analyzing multiple supplier options and pricing structures into a dedicated server system. This separates the complexity from the consumer end, allowing consumers to benefit from comprehensive plan comparison without directly engaging with the complexity of multiple suppliers and pricing models.
Solution Approach 2:
The server system provides universal functionality by handling multiple energy suppliers, various pricing structures, and different consumer preferences through a single integrated platform. It universally processes diverse energy plans and automatically adapts to different consumer needs without requiring separate systems for each supplier or plan type.
3Productivity
If smart devices are used to optimize energy usage, then energy efficiency is improved, but unintentional increase in energy consumption occurs
Solution Approach 1:
The system implements feedback mechanisms where the server continuously monitors energy consumption patterns, pricing data, and device performance. Based on this feedback, the system dynamically adjusts device settings and plan selections to optimize energy usage while preventing unintended consumption increases, ensuring that efficiency improvements translate to actual cost savings.
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.