Domestic Hot Water Forecast Control for Adaptive Tank Heating
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Solution Overview
Problem
Current systems for domestic hot water production and distribution lack accuracy in predicting consumption patterns, leading to inefficient energy use and increased environmental footprint, as they rely on fixed schedules and monitoring systems that do not account for individual user habits.
Innovation Solution
A computer-implemented method using a user-consumption-pattern-determination algorithm trained on historical data from multiple users to accurately forecast domestic hot water consumption, allowing for adaptive control of water heating processes based on individual usage patterns, reducing energy consumption while maintaining user comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed minimum temperature is set throughout the day, then user comfort is maintained, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts the hot water tank temperature based on predicted consumption patterns rather than maintaining a fixed minimum temperature. The controller modifies temperature setpoints in real-time according to forecasted demand, allowing temperature to vary throughout the day while still meeting user needs, thus reducing unnecessary energy consumption.
Solution Approach 2:
The system performs preliminary actions by predicting future hot water consumption patterns and proactively adjusting the tank temperature beforehand. The consumption prediction unit forecasts future demand, and the controller pre-adjusts the temperature to match expected usage, avoiding last-minute heating that would consume more energy.
2Use of energy by moving object
If fixed weekly schedule is used for temperature adjustment, then energy saving is achieved, but accuracy in predicting actual consumption deteriorates
Solution Approach 1:
The system transitions from static weekly schedules to dynamic, real-time consumption prediction. The prediction unit continuously analyzes actual usage patterns and adjusts forecasts accordingly, allowing the system to adapt to changing user behaviors and seasonal variations, thereby improving prediction accuracy while maintaining energy savings.
Solution Approach 2:
The system incorporates feedback mechanisms where actual consumption data is continuously monitored and fed back into the prediction model. This feedback loop allows the system to learn from past predictions versus actual usage, refining its algorithms and improving future prediction accuracy while optimizing energy management.
3Reliability
If hot water is held in reserve in the tank, then user comfort is ensured, but energy loss increases
Solution Approach 1:
The system performs preliminary actions by predicting future consumption and preparing the appropriate amount of hot water in advance. Instead of continuously maintaining large reserves, the system calculates expected demand and heats only the necessary quantity beforehand, reducing the time hot water spends in the tank and minimizing heat loss while still ensuring availability when needed.
Solution Approach 2:
The system dynamically changes temperature parameters based on predicted consumption. Rather than maintaining a constant high temperature for large reserves, the controller adjusts temperature setpoints according to forecasted demand, optimizing the balance between having sufficient hot water available and minimizing energy loss from heat dissipation.
Data Source
AI summary
A computer-implemented method acquires a user consumption pattern of domestic hot water. The method includes acquiring data representing an amount of equivalent energy tapped from a heat storage tank within a first time period, generating a first history or data collection of data representing an amount of cumulative heat tapped from the heat storage tank over a number of first time periods, and acquiring a user consumption pattern of domestic hot water by applying a user-consumption-pattern-determination-algorithm to the generated first history or data collection of data representing amount of cumulative heat tapped from the heat storage tank. The heat storage tank is a pressurized tank. The user-consumption-pattern-determination-algorithm is a time-series-forecast-algorithm trained on history or data collection representing amount of cumulative heat tapped from the heat storage tank or equivalent heat storage tanks, and defining user consumption patterns in one or more machine-learning-algorithms.