Virtual sensors for estimation of energy consumption and thermal comfort in buildings with underfloor heating
Graphical abstract
Virtual sensors are used to estimate room’s heat flow from simple measurements.
Highlights
► Virtual sensors (VS) are used to compare the energy consumption of rooms based on available measurements. ► A generic approach for VS is presented that uses a relative heat coefficient as a flexible input. ► The meaning and calculation of this coefficient depends on the available information. ► Several coefficients are introduced for different use cases such as room size, valve number or heating system size. ► The approach has been proven at a research building with measurements in 58 rooms.
Introduction
Buildings contribute to 42% of western countries energy consumption [1], [2] and it is important to reduce their energy consumption and their related carbon foot-print. A building’s energy-efficiency depends on two aspects [3]. First, the design of a building defines its minimal reachable carbon foot-print. It can be improved by design changes such as adding insulation or making use of renewable energy sources. Second, it is the building’s operation and maintenance that defines how this potential is utilized. Several studies demonstrated that the energy consumption of identical houses may vary by more than a factor of two depending on the occupants’ behavior and the buildings’ operation [3], [4].
Optimal building operation requires identifying the best trade-off between its operating costs, occupant comfort and its energy-efficiency [5], [6]. Most important for a company managing a building are the operational costs such as costs for heating, cooling, hot water, electricity, and maintenance. Next comes occupant comfort, which needs to be provided at a sufficient level. Least important is often the building’s energy efficiency that targets a minimal energy consumption and carbon foot-print. These three objectives are not necessarily related, i.e. it might be cheaper to heat a building over night at low energy prices to avoid heating during the day, even if demand-oriented heating during the day would involve less heat transfer losses and provide more constant comfort to users.
The first step in optimizing a building’s operational consumption is a continuous monitoring of the building’s consumption and conditions. This provides the necessary data for building performance analysis, continuous commissioning, optimizing the operation and users guidance [5], [7], [8]. The large influences of occupant behavior and room usage show the need to analyze a building’s energy consumption down to consumers at room level, in order to identify energy leaks and optimize the building’s performance. New functional buildings are usually fitted with large building automation systems (BAS) that contain several hundreds of devices controlled by a central building management system (BMS) [9]. The market is currently undergoing a change especially in central Europe and most construction contracts focus nowadays on retrofitting existing buildings instead of new construction. Wireless sensor networks are ideally fitted for this market as they allow for easy installation without design changes [10]. This benefit in installation costs is often compensated by higher device costs [11].
Analyzing a building’s energy consumption down to room level requires a large amount of measurement equipment, such as sensors and meters, in each single room. Therefore, detailed building performance analysis is often hampered by high monitoring cost, and practitioners often request alternative cost-efficient methods [12], [13].
Virtual sensor and actuator approaches can reduce equipment requirements. They use a mathematical model of the process to estimate a projected sensor value from other measurements, or convert a virtual control value into another actuator command. Virtual sensor is an established term in process modeling and control for a long time [14], [15]. Virtual sensors are usually used when the targeted monitoring or control value is not directly or only expensively measureable (e.g. hostile environments), or only measureable with large delays (e.g. dead-time processes).
This paper develops an estimation algorithm for a virtual sensor in Section 3 to analyze a building’s energy consumption down to room level. The algorithm is adjustable in its information intake to different sensing and metering equipment available in a building. Therefore, the algorithm offers a flexible usage with rough estimations using few sensors to detailed computations using a high density sensing deployment. The algorithm can be used for offline data analysis and for online monitoring to estimate the heat flows in rooms. Section 4 compares different algorithm variances and discusses their outcome for a real building. The archived user comfort level is also compared, such that a broad set of performance metrics is provided to support building performance analysis, monitoring, and optimization.
The approach is validated using the Environmental Research Institute (ERI) building, located on the campus of the University College Cork, as an existing low-energy building with a hybrid HVAC (heating ventilation air conditioning) system that is introduced in the next section.
Section snippets
The Environmental Research Institute building
Environmentally-friendly buildings combine concepts for energy-efficient buildings with the usage of renewable energy sources to create the energy needed for heating, cooling or domestic hot water [16]. This enables the buildings to operate energy-efficiently and aids in the efforts to preserve a clean environment. In Europe such buildings first need to meet the energy-efficiency standards implementing the European Union’s EPBD (Energy Performance of Buildings Directive) [17]. Additionally,
Assumptions about the room heat consumption and discussion
To identify rooms with unusual high heat usage the room’s heat consumption needs to be analyzed. But, instead of installing heat meters in each room to measure the individual heat consumption, the room’s heat intake is estimated from the overall heat consumption of the underfloor heating system by creating a virtual sensor model using knowledge about the room heating controls.
In the case of the ERI, each room has an individual temperature closed loop control consisting of a temperature sensor,
Comparison of the relative heating coefficients
The introduced approach was applied to the ERI building and measurements from 3 years were evaluated, including the heat meter HP at the heating pump, the heat meter FH of the underfloor heating and temperature sensors in 58 rooms. The control values were computed from these temperature readings with Eq. (5) using the fixed set-points defined in the BMS which is 20 °C for most rooms.
Based on the measurements, the underfloor heating has an absolute heat flow of 95.3 MWh for the 3 years. The
Discussion
Table 2 summarizes the required information, the presumptions, and the concluding notes for the introduced and compared relative heating coefficients. In general, the precision of the approaches increases with the information available and used as demonstrated for the example. Thus, it is usually better to use the commissioned flow rate coefficient than the valve number coefficient. But, if only the valve number is known for a system, then it will still provide a basic estimation of the heat
Conclusion
The introduced virtual sensors approach for estimation of a room’s heat consumption provides a simple and scalable way to estimate the heating energy of rooms from simple temperature readings and a central heat meter. The approach can be applied to any building’s heating system whose rooms are individually controlled, as long as a representative relative heating coefficient can be defined. The various relative heating coefficients also exemplify the flexibility of this approach. Due to the
Future directions and challenges
Tools to evaluate and control energy consumption of buildings will become increasingly important because of limited energy resources and climate change. For their broad application it is needed that they can be used with reliable benefit and low or even no human effort. Their tasks range from detecting construction failures and energy leakages, suggesting and implementing energy optimizations and helping to improve occupants’ comfort.
These tools should become standard in every building. Up to
Acknowledgements
Work in the Strategic Research Cluster ‘ITOBO’ is funded by Grant 06-SRC-I1091 from Science Foundation Ireland (SFI) with additional contributions from five industry partners. Joern Ploennigs thanks the Humboldt-Foundation and the German BMBF for supporting his research in Ireland.
The authors thank Luke Allan and Ena Tobin, Civil Engineering, UCC for their contribution to this research.
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