Machine learning based smart water heater controller using wireless sensor networks
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Solution Overview
Problem
Conventional hot water heaters experience high standby losses due to maintaining a constant high temperature, even when hot water is not needed, leading to inefficiency and increased energy consumption.
Innovation Solution
The Smart Water Heater Controller (SWHC) system uses point-of-use sensors to monitor hot water demand at various locations and adjusts the water heater temperature based on usage patterns, external information, and heat loss characteristics, allowing for efficient temperature control and energy harvesting, thereby reducing unnecessary heating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the water heater maintains a constant high temperature to ensure hot water availability, then hot water readiness is improved, but energy consumption increases due to standby losses
Solution Approach 1:
The water heater temperature is dynamically adjusted based on predicted hot water demand rather than maintaining a constant high temperature. The system uses machine learning models to predict when hot water will be needed and adjusts the tank temperature accordingly, heating water only when demand is anticipated, thus reducing standby energy losses while ensuring hot water availability when needed.
Solution Approach 2:
The system performs preliminary heating actions based on predicted demand patterns. By using point-of-use sensors and machine learning algorithms to anticipate hot water needs, the water heater pre-heats water before actual demand occurs, ensuring hot water is ready when needed while avoiding continuous high-temperature maintenance and associated energy waste.
2Use of energy by moving object
If the water heater temperature is reduced to save energy, then energy consumption decreases, but water temperature adequacy for specific uses deteriorates
Solution Approach 1:
The system provides locally optimized water temperatures at different point-of-use locations based on specific demand requirements. Point-of-use sensors detect actual temperature needs at showers, kitchens, and other locations, and the water heater adjusts its output temperature accordingly, delivering the precise temperature needed for each application rather than using a single high temperature for all uses.
Solution Approach 2:
The water heater dynamically changes its operating temperature parameter based on predicted and actual demand. Rather than maintaining a fixed high temperature, the system adjusts the tank temperature and heating power levels according to the specific requirements detected by point-of-use sensors and predicted by machine learning models, ensuring temperature adequacy for different uses while minimizing energy consumption.
3Measurement precision
If point-of-use sensors are installed to monitor hot water demand at various locations, then temperature control precision is improved, but device complexity increases
Solution Approach 1:
The point-of-use sensors and machine learning system operate autonomously to monitor temperature needs and control water heating without requiring manual intervention or complex user configuration. The sensors self-calibrate and the machine learning models automatically learn usage patterns, reducing the operational complexity despite the additional hardware components.
Solution Approach 2:
The point-of-use sensors serve multiple functions: they monitor water temperature, detect flow patterns, predict demand timing, and provide feedback for controller adjustments. This multi-functionality reduces the need for separate dedicated components for each function, thereby managing system complexity while achieving precise temperature control across multiple locations.
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 SWHC system significantly reduces energy consumption by minimizing standby losses and optimizing water temperature to match specific usage needs, achieving energy savings of approximately 89 kWh/month and lowering costs by avoiding the need for high-insulation water heaters.
Implementation Method 1
a water heater controller configured to automatically control a temperature of water in a water heater tank of a water heater via control of a heating element of the water heater
Data Source
AI summary
Smart hot water heater control can be improved by departing from the conventional approach of monitoring hot water demand at a single point (i.e., the water heater output). Instead, hot water demand is monitored at each location in the building where hot water is used. With this approach, the controller can provide hot water at a temperature suitable for the intended use, e.g., warm water for a bath or shower, and much hotter water for a dishwasher. This advantageously avoids inefficiency due to mixing hot and cold water at a tap to provide temperature control.