Machine learning of heat pump system water usage patterns for optimized heat pump performance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing air-to-water heat pump systems struggle to efficiently manage hot water storage and demand, leading to inefficiencies and increased energy costs due to unpredictable demand patterns and varying ambient temperatures.
Innovation Solution
A machine learning program is utilized to predict hot water demand patterns and adjust the heat pump's operation by monitoring water temperatures with multiple sensors, implementing a load-up cycle to optimize the volume and temperature of stored hot water, minimizing energy costs and ensuring sufficient supply during peak demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the heat pump operates continuously to maintain hot water supply, then the hot water availability is improved, but energy consumption increases
Solution Approach 1:
The machine learning model predicts future hot water demand patterns and schedules heat pump operation in advance to meet predicted demand peaks. The system performs preliminary heating actions before demand occurs, optimizing energy usage by operating during periods of lower cost or better efficiency rather than running continuously.
Solution Approach 2:
The system dynamically adjusts heat pump operation based on real-time conditions including ambient temperature, predicted demand patterns, and current hot water levels. The control strategy transitions from static continuous operation to dynamic demand-responsive operation, improving both reliability and energy efficiency.
2Reliability
If the heat pump operates at high capacity to meet peak demand, then hot water supply reliability is improved, but energy costs increase
Solution Approach 1:
The system uses machine learning to predict demand peaks and schedules high-capacity heat pump operation before these peaks occur. By performing preliminary heating actions during periods of lower cost or better efficiency, the system avoids the need for expensive peak-capacity operation during actual demand periods.
Solution Approach 2:
The machine learning model continuously learns from historical data and system performance to autonomously optimize operation schedules. The system serves itself by automatically adjusting operation strategies based on learned patterns, eliminating the need for manual intervention and achieving cost-effective operation.
3Measurement precision
If multiple temperature sensors are deployed to accurately monitor hot water volume, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The hot water storage system is divided into multiple stratified layers, with temperature sensors positioned at different heights to measure temperature at each layer. This segmentation allows the system to accurately determine the volume of hot water at different temperature levels by identifying which layers exceed the required temperature threshold, providing precise measurement without requiring a single complex sensor.
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
The system effectively predicts and manages hot water demand, reducing energy costs and improving efficiency by adjusting the heat pump's output based on ambient conditions and demand patterns, ensuring sufficient hot water supply while minimizing energy waste.
Implementation Method 1
air-to-water heat pump systems have been developed. Known heat pump systems may be utilized to heat water for use in buildings
Data Source
AI summary
An air to water heat pump system is configured to minimize operating cost of the system utilizing one or more of hot water demand patterns, present or predicted ambient conditions, and/or electrical power cost. The system may be configured to reduce production and/or storage of hot water during periods of low demand, and increase production and storage of hot or hotter water immediately prior to a period of predicted high demand for hot water. The system may be configured to take into account ambient weather conditions to increase production and storage of hot water during favorable ambient conditions, and/or to increase production and storage of hot water prior to predicted cold ambient conditions.