Built Environment Energy Control for Time-Variant Demand
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Solution Overview
Problem
The integration of renewable energy sources into energy management systems poses challenges due to their intermittent nature, leading to time-variant energy demand fluctuations, which complicates energy storage and pricing, and existing solutions like batteries have limitations in effective installation and economic feasibility.
Innovation Solution
A computer-implemented method and system that collects and parses statistical data from energy consuming devices, user behavior, energy storage and supply means, environmental sensors, and pricing models to prioritize instructional control strategies, optimizing the operational behavior of energy consuming devices and reducing time-variant energy demand by regulating energy consumption and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If renewable energy sources are integrated into energy management systems, then environmental sustainability is improved, but time-variant energy demand fluctuations increase
Solution Approach 1:
The system performs preliminary actions by collecting statistical data from multiple sources (energy consuming devices, user behavior, environmental sensors) and developing energy usage profiles in advance. This allows the system to predict energy demand patterns and prioritize control strategies before peak demand occurs, thereby stabilizing energy demand while using renewable sources.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting data from energy metering devices, occupancy detection means, and environmental sensors. This real-time feedback enables the system to adjust control strategies dynamically, balancing renewable energy integration with demand stability through iterative optimization of energy usage patterns.
2Reliability
If energy storage means are deployed to store energy, then energy supply reliability is improved, but installation costs and system complexity increase
Solution Approach 1:
The system enables self-service by allowing energy consuming devices to automatically adjust their operation based on prioritized control strategies. Instead of requiring complex centralized energy storage systems, individual devices self-regulate their energy consumption patterns according to pre-determined priorities, reducing the need for large-scale energy storage infrastructure.
Solution Approach 2:
The system segments the energy management approach by categorizing energy consuming devices into different priority groups and applying differentiated control strategies to each segment. This segmentation allows for simpler, more targeted energy management without requiring comprehensive energy storage capacity for the entire system.
3Measurement precision
If comprehensive statistical data is collected and parsed, then energy management precision is improved, but data processing time and computational resources increase
Solution Approach 1:
The system changes parameters by transforming raw statistical data into standardized energy usage profiles with specific parameters and priorities. This parameter transformation occurs in advance, allowing the system to work with pre-processed, prioritized data during energy management operations, significantly reducing real-time data processing requirements while maintaining high measurement precision.
Data Source
AI summary
The present disclosure provides a computer-implemented method for prioritizing one or more instructional control strategies to reduce time-variant energy demand of a built environment associated with renewable energy sources. The computer-implemented method includes collection of a first set of statistical data, fetching of a second set of statistical data, accumulation of a third set of statistical data, reception of a fourth set of statistical data and gathering of fifth set of statistical data. Further, the computer-implemented method includes parsing and comparison of the first set of statistical data, the second set of statistical data, the third set of statistical data, the fourth set of statistical data and the fifth set of statistical data. In addition, the computer-implemented method includes identification and prioritization of one or more instructional control strategies to reduce the time-variant energy demand associated with the built environment.