Data-Driven Strategies for Heat Pump Systems
A journey from inadequate data towards knowledge-based services
Time: Fri 2024-10-11 10.00
Location: Kollegiesalen, Brinellvägen 8, Stockholm
Language: English
Subject area: Energy Technology
Doctoral student: MSC Yang Song , Tillämpad termodynamik och kylteknik
Opponent: Professor Christof Wittwer, Fraunhofer ISE and University of Freiburg
Supervisor: Universitets lektor Hatef Madani Larijani, Tillämpad termodynamik och kylteknik; Dr. Davide Rolando, Tillämpad termodynamik och kylteknik
Abstract
Integrating high-efficiency heat pumps to renewable electricity will significantly accelerate decarbonization progress. Despite the advancements in smart sensors and communication technologies that enhance data generation in heat pump units, much of this data remains underutilized for performance analysis. The primary issue is that the data often exhibits incompleteness, inconsistency, and inaccuracies. Consequently, data collection and storage impose an economic burden on manufacturers and end-users, and they have limited returns. This thesis aims to unlock the potential of various data resources, delivering knowledge-based services and addressing gaps in data availability and utilization.
The dissertation introduces the Data-Information-Knowledge-Service (DIKS) framework as an adaptation of the traditional Data-Information-Knowledge-Wisdom (DIKW) hierarchy, emphasizing the practical application of turning inadequate data into knowledge-based services in heat pump technologies. The transformative process within the DIKS pyramid is illustrated, detailing how each layer converts inadequate raw heat pump data into actionable, knowledge-based services. It begins with the aggregation and integration of various data types, followed by advanced processing techniques to refine data quality and identify significant patterns. This foundation of knowledge is then applied to improve heat pump services, demonstrating the practical benefits of this structured approach.
Five different scenarios are examined utilizing different types of data, including high-quantity, low-quality in-situ measurements, high-quality, low-quantity lab data, and technical specifications. The first scenario, utilizing in-situ field measurement data, develops an artificial neural network (ANN) model to create soft sensors that compensate for the absence of costly physical sensors in heat pump systems. These soft sensors use incomplete data to accurately estimate essential heat pump parameters, supporting functions like operational monitoring, fault detection, smart energy management, and developing digital twins. The second scenario, also utilizing in-situ field measurement data, focuses on models that prioritize minimal input features, enhancing models' usability across various installations. These models estimate power consumption effectively and compensate for the lack of physical power meters, thus facilitating network planning and smart control, etc. In the third scenario, transfer learning techniques are employed to estimate heat pump performance with limited lab data, particularly for natural refrigerants in the context of the phasing out the fluorinated refrigerants. This approach utilizes knowledge from existing refrigerant data to improve model reliability and accuracy, aiding in refrigerant selection. The fourth scenario develops polynomial regression models from technical specifications to evaluate heat pump performance without dynamic measurements. These models assist in tracking the system performance. The final scenario introduces semi-empirical models that use thermodynamic and heat transfer correlations to enhance the understanding of physical meaning. These models are designed with reduced parameters, improving services such as fault diagnosis and system maintenance.
In conclusion, this thesis identifies common status and key issues related to inadequate data from heat pump systems. Furthermore, this thesis proposes solutions to transfer inadequate data to useful structured information and develops data-driven models according to the characteristics of different data types. The models are validated against measurements, demonstrating accurate results. This thesis demonstrates the application of the DIKS framework to effectively harness underutilized inadequate data from heat pump systems, which not only provides insights to alleviate the economic burdens placed on manufacturers and/or users related to data cost but also enable the services of heat pump systems.