DEVELOPMENT OF A SCALABLE CLOUD-BASED FRAMEWORK FOR AUTOMATED CALIBRATION OF DIGITAL TWINS
Abstract
Two preconditions for deploying Digital Twins are calibrated process models and IoT-based infrastructures for communication between digital and real systems enabling services like operational optimization. In particular, digital systems predict the plant behavior, draw conclusions for control adjustments, and optimize the real system operation via the communication infrastructure. This work presents a cloud-based framework to fulfill both preconditions applied to heat pumps in residential buildings. In a case study, we investigate repeated calibration by connecting real and digital systems. Using system supply temperature as the target variable and RMSE for evaluation, we automatically encounter repetitive experimental data with RMSE<3 K.
Authors 4
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Affiliation as printed
Institute for Energy Efficient Buildings and Indoor Climate RWTH Aachen University , Germany,
Institute for Energy Efficient Buildings and Indoor Climate RWTH Aachen University, Germany,
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Affiliation as printed
Institute for Energy Efficient Buildings and Indoor Climate RWTH Aachen University , Germany,
Institute for Energy Efficient Buildings and Indoor Climate RWTH Aachen University, Germany,
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Affiliation as printed
Institute for Energy Efficient Buildings and Indoor Climate RWTH Aachen University , Germany,
Institute for Energy Efficient Buildings and Indoor Climate RWTH Aachen University, Germany,
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Affiliation as printed
Institute for Energy Efficient Buildings and Indoor Climate RWTH Aachen University , Germany,
Institute for Energy Efficient Buildings and Indoor Climate RWTH Aachen University, Germany,
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