Hot Water Storage Control for Adaptive Boiling Load Prediction
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Solution Overview
Problem
Existing hot water supply systems face energy inefficiencies due to excessive boiling during low-night electricity prices, leading to potential hot water shortages and impaired energy saving properties, especially in systems with low-capacity storage tanks or heat source units.
Innovation Solution
A hot water supply system with a controller that analyzes historical load data, predicts daily hot water supply loads, and adjusts operation plans based on real-time load results and stored water levels, optimizing heat generation for each time slot to minimize energy waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If boiling is performed at late night when electricity unit price is low, then energy cost is reduced, but excessive boiling occurs leading to impaired energy saving property
Solution Approach 1:
The system performs preliminary classification of hot water supply load data into multiple groups based on past patterns, and generates operation plans in advance for different scenarios. This allows the system to prepare appropriate boiling schedules before actual operation, avoiding both excessive and insufficient boiling.
Solution Approach 2:
The system continuously monitors actual hot water supply load results and compares them with predicted values. Based on this feedback, the operation plan correction unit adjusts subsequent operation plans to match actual demand patterns, preventing excessive boiling while ensuring adequate hot water supply.
2Reliability
If boiling is performed multiple times in one day for systems with low-capacity storage tanks, then hot water supply demand is met, but energy saving property is impaired
Solution Approach 1:
The system dynamically adjusts the operation plan based on actual hot water supply load results. The operation plan correction unit modifies subsequent boiling schedules in real-time, transitioning from static pre-planned operations to adaptive dynamic control that optimizes energy consumption while meeting demand.
Solution Approach 2:
The system changes operational parameters such as boiling amount and timing based on classified load patterns. By adjusting these parameters according to actual demand rather than using fixed schedules, the system reduces unnecessary reheating operations and improves energy efficiency.
3Reliability
If excessive boiling is performed to avoid hot water shortage, then hot water supply reliability is improved, but energy waste increases
Solution Approach 1:
Instead of performing excessive boiling to ensure supply, the system applies partial action by classifying load patterns and generating targeted operation plans that match actual demand. This prevents both over-supply and under-supply by applying the right amount of heating action.
Solution Approach 2:
The system replaces mechanical judgment and manual adjustment with an automated intelligent control system that uses data analysis, pattern recognition, and automatic plan generation. This substitution enables precise control that avoids both excessive and insufficient heating operations.
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 saving by dynamically adjusting heat generation according to actual demand, reducing unnecessary reheating and maintaining adequate hot water supply while minimizing energy consumption.
Implementation Method 1
a boiling unit that is a heating source that heats the water stored in the hot water storage tank
Implementation Method 2
a heat source unit such as a heat pump or a boiler... heats the water stored in the hot water storage tank
Data Source
AI summary
An operation plan correction unit is included which, after start of operation based on an operation plan, predicts a subsequent hot water supply load at a predetermined day on the basis of a hot water supply load result at the predetermined day, and changes a subsequent operation plan at the predetermined day generated by an operation plan generation unit, on the basis of the hot water supply load predicted again and a remaining amount of stored hot water in a hot water storage tank.