Model Predictive Control for Energy-Efficient Fluid Heating
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Solution Overview
Problem
Despite efforts to introduce energy-efficient technologies, residential and commercial buildings continue to experience rising energy consumption due to increasing electricity demand, necessitating methods to reduce energy usage and implement more efficient systems.
Innovation Solution
A method for controlling fluid temperature using a model predictive controller that optimizes future fluid temperature set-points based on historical usage data and energy predictions, minimizing input energy required by adjusting for energy losses and inputs through a series of fluid nodes, and incorporating both resistive heating elements and heat pumps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If model predictive control is implemented to optimize heating operations, then energy consumption is reduced, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by predicting future hot water usage requirements and pre-heating water in advance during off-peak hours. The MPC controller calculates optimal set-point temperatures ahead of time based on predicted demand, allowing the water heater to prepare hot water before it is actually needed, thereby reducing peak-hour energy consumption while maintaining system simplicity through advance planning
Solution Approach 2:
The system implements dynamic operation by continuously adjusting heating operations based on real-time conditions and predictions. The MPC controller dynamically modifies set-point temperatures and heating power levels according to predicted hot water demand, ambient conditions, and time-of-use pricing signals, enabling the system to adapt its behavior optimally rather than operating with fixed parameters
2Use of energy by stationary object
If predictive control strategies are used to shift load to off-peak hours, then energy costs are reduced, but control complexity increases
Solution Approach 1:
The MPC controller incorporates feedback mechanisms by continuously monitoring actual hot water usage, comparing it with predictions, and adjusting future set-point calculations accordingly. The system uses feedback from time-of-use pricing signals, ambient temperature measurements, and actual consumption patterns to refine its predictive models and optimize control decisions, reducing energy costs while managing control complexity through systematic feedback loops
Solution Approach 2:
The system changes operational parameters dynamically by adjusting set-point temperatures, heating power levels, and prediction time horizons based on varying conditions. The MPC controller modifies these parameters in response to time-of-use pricing signals, predicted demand patterns, and environmental conditions, enabling cost-effective load shifting while managing complexity through structured parameter adaptation
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 achieves significant energy savings, up to 20% in simulations, while maintaining thermal comfort, and can incorporate time-of-use pricing to shift loads from peak to off-peak, effectively reducing energy consumption in buildings.
Implementation Method 1
a heating element configured to heat the fluid
Implementation Method 2
both the resistive heating element and the heat pump are configured to heat the water
Data Source
AI summary
Model predictive control methods are disclosed which provide, among other things, efficient strategies for controlling heat-transfer to a fluid.