A prototype of an indoor monoblock heat pump was tested at multiple ambient temperatures, to determine heat output, COP and the impact of defrosting events. Component efficiencies and the ice accumulation process were analysed. Options to improve performance were suggested.
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The domestic use of natural gas for heating is the prevalent option in the Netherlands. However, heat pumps will be mandatory in most Dutch households by 2026. Therefore, insights are needed in how citizens perceive this technology, by taking into account various societal, technological, economic, environmental, and political aspects. Our research offers a systematic investigation of the multiple viewpoints of heat pump users and their neighbors in Groningen, northern Netherlands. Using Q-methodology, we identified three distinct but interrelated and shared viewpoints: the realistic users, the hesitant neighbors and the enthusiastic advocates. All three shared viewpoints incorporate social influence and cognitive considerations, with the positive environmental impacts of heat pumps being highlighted in unison. Cognitive considerations relate mainly to technical and economic concerns. Social influence considerations often hint at the necessity of making prior agreements with the neighbors. We argue that the findings of this study can support policymakers toward the development of an integrated heat transition strategy.
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Research has been done on how the implementation of heat pumps in dwellings could have the maximum impact on energy savings.
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Residential electricity distribution grid capacity is based on the typical peak load of a house and the load simultaneity factor. Historically, these values have remained predictable, but this is expected to change due to increasing electric heating using heat pumps and rooftop solar panel electricity generation. It is currently unclear how this increase in electrification will impact household peak load and load simultaneity, and hence the required grid capacity of residential electricity distribution grids. To gain better insight, transformer and household load measurements were taken in an all-electric neighborhood over a period of three years. These measurements were analyzed to determine how heat pumps and solar panels will alter peak load and load simultaneity, and hence grid capacity requirements. The impacts of outdoor effective temperature and solar panel orientation were also analyzed. Moreover, the potential for smart grids to reduce grid capacity requirements was examined.
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The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
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In practice, faults in building installations are seldom noticed because automated systems to diagnose such faults are not common use, despite many proposed methods: they are cumbersome to apply and not matching the way of thinking of HVAC engineers. Additionally, fault diagnosis and energy performance diagnosis are seldom combined, while energy wastage is mostly a consequence of component, sensors or control faults. In this paper new advances on the 4S3F diagnose framework for automated diagnostic of energy waste in HVAC systems are presented. The architecture of HVAC systems can be derived from a process and instrumentation diagram (P&ID) usually set up by HVAC designers. The paper demonstrates how all possible faults and symptoms can be extracted on a very structured way from the P&ID, and classified in 4 types of symptoms (deviations from balance equations, operational states, energy performances or additional information) and 3 types of faults (component, control and model faults). Symptoms and faults are related to each other through Diagnostic Bayesian Networks (DBNs) which work as an expert system. During operation of the HVAC system the data from the BMS is converted to symptoms, which are fed to the DBN. The DBN analyses the symptoms and determines the probability of faults. Generic indicators are proposed for the 4 types of symptoms. Standard DBN models for common components, controls and models are developed and it is demonstrated how to combine them in order to represent the complete HVAC system. Both the symptom and the fault identification parts are tested on historical BMS data of an ATES system including heat pump, boiler, solar panels, and hydronic systems. The energy savings resulting from fault corrections are estimated and amount 25%. Finally, the 4S3F method is extended to hard and soft sensor faults. Sensors are the core of any FDD system and any control system. Automated diagnostic of sensor faults is therefore essential. By considering hard sensors as components and soft sensors as models, they can be integrated into the 4S3F method.
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Thermal comfort is determined by the combined effect of the six thermal comfort parameters: temperature, air moisture content, thermal radiation, air relative velocity, personal activity and clothing level as formulated by Fanger through his double heat balance equations. In conventional air conditioning systems, air temperature is the parameter that is normally controlled whilst others are assumed to have values within the specified ranges at the design stage. In Fanger’s double heat balance equation, thermal radiation factor appears as the mean radiant temperature (MRT), however, its impact on thermal comfort is often ignored. This paper discusses the impacts of the thermal radiation field which takes the forms of mean radiant temperature and radiation asymmetry on thermal comfort, building energy consumption and air-conditioning control. Several conditions and applications in which the effects of mean radiant temperature and radiation asymmetry cannot be ignored are discussed. Several misinterpretations that arise from the formula relating mean radiant temperature and the operative temperature are highlighted, coupled with a discussion on the lack of reliable and affordable devices that measure this parameter. The usefulness of the concept of the operative temperature as a measure of combined effect of mean radiant and air temperatures on occupant’s thermal comfort is critically questioned, especially in relation to the control strategy based on this derived parameter. Examples of systems which deliver comfort using thermal radiation are presented. Finally, the paper presents various options that need to be considered in the efforts to mitigate the impacts of the thermal radiant field on the occupants’ thermal comfort and building energy consumption.
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Understanding sludge rheology and optimizing equipment performance is crucial for energy efficiency in wastewater treatment plants (WWTPs). This study examined sludge rheology after thermal hydrolysis pretreatment (THP) at 60, 80, and 120 ◦C for 2 h, followed by anaerobic digestion (AD) at 37 ◦C for 20 days, and assessed impacts on pump and agitator performance. Post-treatment, sludge showed reduced viscosity and improved flowability, indicated by changes in Herschel-Bulkley parameters, enhancing pump and agitator efficiency, particularly at 120 ◦C. These rheological improvements were correlated to the solubilization of sludge components after THP and solids reduction after AD, highlighting the interconnectedness of rheology and treatment outcomes. Despite high heat demands, an energy balance showed that THP scenarios, especially at 120 ◦C, had lower energy requirements for pumps and agitators, leading to energy savings without increased heat consumption. These findings underscore the influence of rheological changes in improving energy efficiency in WWTPs.
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The neighborhood of Houtlaan in Assen, the Netherlands, has ambitious targets for reducing the neighborhood’scarbon emissions and increasing their production of their own, sustainable energy. Specifically, they wish toincrease the percentage of houses with a heat pump, electric vehicle (EV) and solar panels (PV) to 60%, 70%and 80%, respectively, by the year 2030. However, it was unclear what the impacts of this transition would be onthe electricity grid, and what limitations or problems might be encountered along the way.Therefore, a study was carried out to model the future energy load and production patterns in Houtlaan. Thepurpose of the model was to identify and quantify the problems which could be encountered if no steps are takento prevent these problems. In addition, the model was used to simulate the effectiveness of various proposedsolutions to reduce or eliminate the problems which were identified.
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