Shallow geothermal energy efficient utilization and storage system and method based on deep learning optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing scheduling mode of geothermal energy heat exchange and storage is rigidified, making it difficult to achieve a stable and comfortable experience for urban spaces while efficiently utilizing clean energy.
Innovation Solution
An efficient shallow geothermal energy utilization and storage method based on deep learning optimization, which involves acquiring current time information, performing matching analysis with energy consumption demand curves, calculating energy storage information, and replacing heat exchange components at optimal times to predict energy consumption and adjust accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If rigidified scheduling mode is used for geothermal energy heat exchange and storage, then system operation is simplified, but stable and comfortable experience for urban spaces cannot be achieved
Solution Approach 1:
The patent applies dynamics by transitioning from a rigidified scheduling mode to a dynamic optimization mode using deep learning algorithms. The system continuously adjusts heat exchange and storage scheduling based on real-time energy consumption patterns, weather conditions, and building thermal characteristics, enabling both ease of operation through automation and reliability through adaptive response to changing conditions.
Solution Approach 2:
The patent implements feedback mechanisms by using deep learning models that continuously monitor and analyze energy consumption data, temperature patterns, and operational parameters. The system learns from historical and real-time data to optimize scheduling decisions, providing stable and comfortable urban space temperatures while simplifying operation through intelligent automated control that adapts to actual system performance.
2Ease of operation
If manual regulation is used for geothermal energy utilization, then system control is straightforward, but workload is high and efficiency is reduced
Solution Approach 1:
The patent applies self-service by implementing an automated deep learning-based optimization system that performs scheduling and regulation tasks without manual intervention. The system autonomously analyzes energy consumption data, predicts thermal demands, and adjusts heat exchange and storage operations to maximize geothermal utilization efficiency, thereby reducing manual workload while significantly improving productivity through intelligent automated decision-making.
3Device complexity
If simple scheduling mode is used, then system complexity is reduced, but accuracy of energy storage and supply mode switching is insufficient
Solution Approach 1:
The patent replaces simple mechanical scheduling systems with an intelligent deep learning-based optimization system. The deep learning model processes multiple input parameters including energy consumption data, weather forecasts, building thermal models, and real-time sensor readings to accurately determine optimal switching times between energy storage and supply modes, achieving high switching accuracy without proportionally increasing system complexity through software-based intelligence.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method reduces the workload of manual regulation, improves the efficiency and intelligence of geothermal utilization, and enhances the accuracy of energy storage and supply mode switching, ensuring that energy is stored and supplied effectively without being below the high-efficiency threshold.
Implementation Method 1
A phase-change material is a substance which will be subjected to phase state transition under a certain temperature condition. It can absorb or release certain latent heat during the phase state transition
Implementation Method 2
It can absorb or release certain latent heat during the phase state transition
Implementation Method 3
The heat conductivity coefficient of the phase-change material is different from that of concrete, the heat conductivity of the material can be changed by adding the concrete
Data Source
AI summary
The present application relates to an efficient shallow geothermal energy utilization and storage system and method based on deep learning optimization. It includes the steps of obtaining current time information; determining theoretical energy consumption demand curve information; acquiring the heat exchange component number information and a corresponding single-member potential energy information; calculating single-member energy storage information according to the single-member potential energy information and preset efficiency threshold information; adding single-member energy storage information corresponding to the heat exchange component number information to obtain total stored energy information; calculating replacement time information according to the theoretical energy consumption demand curve information and the total stored energy information; and replacing the heat exchange component corresponding to the heat exchange component number information at the replacement time.