Water sourced heat pump (WSHP) system optimization using reinforcement learning (RL) agent
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Solution Overview
Problem
Water sourced heat pump (WSHP) systems face inefficiencies due to varying water temperatures affecting heat exchange, leading to increased energy consumption and reduced efficiency, especially in extreme climates, and require supplemental heating or cooling mechanisms, which further increase energy costs.
Innovation Solution
Implementing a reinforcement learning (RL) agent to analyze real-time state variables and optimize water loop temperature and flow rate in WSHP systems, using trained RL agents to generate action variables and reward functions that minimize energy costs and thermal discomfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the water temperature is outside the optimal range (too low for heating or too high for cooling), then the WSHP system can maintain indoor comfort levels, but energy consumption increases due to harder compressor work and supplemental heating/cooling mechanisms
Solution Approach 1:
The patent implements dynamic adjustment of water loop temperature and flow rate based on real-time conditions. The system continuously monitors water temperature, building thermal demands, and environmental conditions, then dynamically optimizes operating parameters to maintain comfort while minimizing energy consumption. This resolves the contradiction by adapting the system operation rather than relying on fixed setpoints that may become suboptimal under varying conditions.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor indoor temperature, water loop conditions, and energy consumption in real-time. This feedback enables the control system to adjust compressor operation and water flow rates dynamically, ensuring comfort maintenance while optimizing energy efficiency. The feedback loop allows the system to respond to changing conditions and prevent excessive energy consumption before it occurs.
2Power
If the WSHP system operates with multiple units (hundreds of WSHPs) to serve large buildings, then heating and cooling capacity increases, but system complexity and coordination challenges increase
Solution Approach 1:
The patent implements a centralized control system that serves multiple WSHPs with a unified optimization algorithm. This universal control approach coordinates hundreds of individual units while maintaining consistent optimization criteria across the entire system. The system handles diverse operating conditions and unit states through a single multi-functional control framework, reducing complexity compared to individual control of each unit.
Solution Approach 2:
The system merges control functions for multiple WSHPs into a unified control architecture. By combining individual unit control with system-wide optimization, the patent manages the complexity of coordinating hundreds of units through integrated control logic that considers aggregate system performance while maintaining individual unit responsiveness.
3Power
If the WSHP system operates in extreme climates with significant temperature differentials, then heating or cooling effectiveness increases, but compressor work increases leading to reduced efficiency
Solution Approach 1:
The patent dynamically changes operating parameters including water loop temperature setpoints and flow rates based on ambient conditions and building demands. In extreme climates, the system adjusts these parameters to optimize the balance between achieving required heating/cooling effectiveness and maintaining compressor efficiency, rather than operating at fixed parameters that may become suboptimal under extreme conditions.
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
Optimizes water loop temperature and flow rate to enhance energy efficiency, reduce thermal discomfort, and lower operational costs by dynamically adjusting to environmental conditions and building demands.
Implementation Method 1
The WSHP extracts heat from the water source during the heating season and transfers it into the building to provide warmth
Implementation Method 2
The WSHP system uses water as the heat exchange medium instead of air
Implementation Method 3
During the cooling season, the WSHP removes heat from the building and releases it into the water source
Implementation Method 4
The WSHP uses closed-loop systems to circulate water between the heat pump and the water source
Implementation Method 5
Such closed-loop systems allow the heat pump to continuously exchange heat with the water source
Data Source
AI summary
A method for optimizing a water sourced heat pump (WSHP) system using reinforcement learning (RL) agent is disclosed. The method comprises deploying, via at least one processor, a trained RL agent in the WSHP system comprising a plurality of WSHPs; analyzing state variables associated with the WSHP system in real-time, using the trained RL agent, generating, via the at least one processor, one or more action variables using the trained RL agent based at least on the analyzed state variables associated with the WSHP system, wherein the one or more action variables comprises at least one of water loop temperature and water loop flow rate; generating at least one reward function based on the generated one or more action variables, and optimizing at least one of the water loop temperature and the water loop flow rate of the WSHP system based on the generated at least one reward function.