Integrated Simulation and Calibration Framework for Heating System Optimization.

Area of Science:

• Building energy systems
• Control engineering
• Sustainable energy

Background:

• Heating systems face inefficiencies due to suboptimal controller parameters.
• Existing monitoring systems lack actionable improvement suggestions.
• Accurate digital twins are hindered by unavailable critical data like water consumption.

Purpose of the Study:

• To develop a simulation and calibration framework for heating systems.
• To enable optimization of controller parameters for improved energy efficiency.
• To address data limitations in digital twin modeling for buildings.

Main Methods:

• Utilized coarse models for quantitative predictions and qualitative assessments.
• Developed a preprocessing module for data augmentation.
• Implemented automatic model calibration using measured data.
• Evaluated controller parameter optimization for reduced gas consumption.

Main Results:

• Achieved acceptable fit with partially incomplete measured data using coarse models and preprocessing.
• Demonstrated successful calibration of the heating system model.
• Successfully optimized controller parameters to reduce simulated boiler gas consumption.

Conclusions:

• Coarse digital twin models, augmented with preprocessing, can effectively model incomplete data.
• The framework enables accurate calibration and optimization of heating system controllers.
• This approach enhances sustainability and CO2 efficiency in building heating systems.