Heat Pump Water Provision System with Predictive Occupancy Control
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Solution Overview
Problem
Existing systems for providing heated water are inefficient and fail to adapt to varying household or commercial requirements, leading to energy and water wastage, as they do not account for daily or weekly variations in occupancy and usage patterns.
Innovation Solution
A computer-implemented method using machine learning algorithms to predict occupancy and adjust the operation of a heat pump-based water provision system by pre-charging a thermal energy storage medium based on expected arrival times and usage patterns, optimizing energy usage by shifting demand to off-peak hours.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a heat pump is used to provide heated water, then energy efficiency is improved, but the response time to heat water is delayed
Solution Approach 1:
The system performs preliminary heating of water using the heat pump before the actual hot water demand occurs. The machine learning model predicts occupancy and anticipates hot water needs, activating the heat pump in advance to pre-heat water in the thermal energy storage medium, thus resolving the delay issue while maintaining energy efficiency.
2Loss of time
If energy is consumed during peak hours to provide heated water, then immediate water heating is achieved, but energy cost increases
Solution Approach 1:
The system pre-heats water during off-peak hours when energy costs are lower, using predictive algorithms to anticipate hot water demand. This allows the system to avoid expensive peak-hour energy consumption while still meeting immediate hot water needs, as the thermal energy storage medium maintains heated water ready for use.
Solution Approach 2:
The system continuously monitors occupancy patterns, usage behavior, and energy prices, using this feedback to optimize when to activate the heat pump. The machine learning model adjusts heating schedules based on real-time and historical data, ensuring energy is consumed at optimal times while maintaining water heating readiness.
3Reliability
If the water provision system operates continuously to meet varying demand, then water availability is improved, but energy waste increases
Solution Approach 1:
The system dynamically adjusts its operation based on predicted occupancy and hot water demand rather than running continuously. The machine learning model continuously updates predictions based on changing patterns, allowing the heat pump to operate only when and where needed, thus maintaining water availability while minimizing energy waste through adaptive, non-static operation.
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 approach enhances energy efficiency by reducing delays in heating water and minimizing energy consumption during peak hours, ensuring a consistent supply of heated water while conserving energy and water.
Implementation Method 1
a water provision system comprising a heat pump configured to transfer thermal energy from the surrounding to a thermal energy storage medium
Data Source
AI summary
A computer-implemented method predictively prepares a water provision system installed in a building. The water provision system includes a heat pump configured to transfer thermal energy from the surrounding to a thermal energy storage medium and a control module configured to control operation of the heat pump. The water provision system is configured to provide water heated by the thermal energy storage medium to an occupant of the building at one or more water outlets. The method is performed by the control module and includes: receiving a current location of the occupant, estimating an expected arrival time for the occupant to arrive at the building based on the current location, and determining an expected occupancy of the building based on the expected arrival time.