Hot Water Demand Prediction for Multi-Household Supply Control
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Solution Overview
Problem
Existing hot water supply systems face challenges in accurately predicting and managing hot water demand across multiple targets, leading to inefficiencies and increased electricity costs, particularly in multi-household settings where demand varies significantly.
Innovation Solution
A hot water supply system that includes a heat pump heating apparatus, a tank, and a control unit with machine learning capabilities to predict total hot water demand based on time-series data from multiple supply targets, optimizing the operation of the heating apparatus to minimize electricity costs and prevent hot water shortages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a hot water supply system serves multiple households with varying demand patterns, then the system must accommodate diverse hot water needs, but this leads to inaccurate demand prediction and increased electricity costs
Solution Approach 1:
The system segments hot water demand prediction by individual household and time period, collecting separate time-series data for each household's hot water supply targets. This segmentation allows the estimation unit to learn and predict demand patterns for each household independently, then aggregate these predictions to achieve accurate total demand forecasting for multiple households.
2Reliability
If the heating apparatus operates continuously to ensure hot water availability, then hot water shortages are prevented, but electricity costs increase
Solution Approach 1:
The estimation unit predicts future hot water demand in advance by analyzing historical time-series data patterns. Based on these predictions, the control unit schedules heating operations to occur before peak demand periods, pre-heating water in the tank. This preliminary action ensures hot water availability during high-demand periods while avoiding continuous operation, thereby reducing electricity costs.
3Measurement precision
If the system collects and processes time-series data from multiple supply targets, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system merges data collection and processing functions into a centralized estimation unit that receives time-series data from multiple supply targets across different households. This unit integrates the data streams and applies learning algorithms to predict total hot water demand, simplifying the overall system architecture while maintaining high prediction accuracy through combined multi-source data analysis.
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 system improves prediction accuracy of hot water demand and reduces electricity costs by optimizing heating operations based on learned patterns, ensuring efficient supply and minimizing the risk of running out of hot water, even in diverse demand scenarios.
Implementation Method 1
a heating apparatus configured to heat water
Data Source
AI summary
A hot water supply system includes a hot water supply apparatus, a plurality of supply paths, and an estimation unit. The hot water supply apparatus includes a heating apparatus configured to heat water, a tank configured to store the water heated by the heating apparatus, and a water circuit configured to circulate the water in the tank. Each supply path is coupled to a corresponding one of a plurality of hot water supply targets and is configured to supply the water from the tank. The estimation unit is configured to predict a total amount of hot water supply demand, based on time-series data of a first index that indicates an amount of heat of water used in each of the plurality of hot water supply targets.