This paper presents a case study where a model predictive control (MPC) logic is developed for energy flexible operation of a space heating system in an educational building. A Long Short-Term Memory Neural Network (LSTM) surrogate model is trained on the output of an EnergyPlus building simulation model. This LSTM model is used within an MPC framework where a genetic algorithm is used to optimize setpoint sequences. The EnergyPlus model is used to validate the performance of the control logic. The MPC approach leads to a substantial reduction in energy consumption (7%) and energy costs (13%) with improved comfort performance. Additional energy costs savings are possible (7–16%) if a sacrifice in indoor thermal comfort is accepted. The presented method is useful for developing MPC systems in the design stages where measured data is typically not available. Additionally, this study illustrates that LSTM models are promising for MPC for buildings.
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gains and internal gains from appliances,heat gains from occupants are an importantsource contributing to space heating indomestic buildings. It is necessary toconsciously consider all of these heat gainswhen aiming at accurately estimating theheat loss coefficient (HLC) of a building.Whilst sensor technology and algorithms areavailable to quantify the contribution of theheating system, solar gains and electricalappliances, the accurate estimation of thesensible heat gain from occupants ischallenging due to its stochastic character.
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Ageing brings about physiological changes that affect people’s thermal sensitivity and thermoregulation. The majority of older Australians prefer to age in place and modifications to the home environment are often required to accommodate the occupants as they age and possibly become frail. However, modifications to aid thermal comfort are not always considered. Using a qualitative approach this study aims to understand the thermal qualities of the existing living environment of older South Australians, their strategies for keeping cool in hot weather and warm in cold weather and to identify existing problems related to planning and house design, and the use of heating and cooling. Data were gathered via seven focus group sessions with 49 older people living in three climate zones in South Australia. The sessions yielded four main themes, namely ‘personal factors’, ‘feeling’, ‘knowing’ and ‘doing’. These themes can be used as a basis to develop information and guidelines for older people in dealing with hot and cold weather. Original publication at MDPI: https://doi.org/10.3390/ijerph16060935 © 2018 by the authors. Licensee MDPI.
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Cities worldwide are growing at unprecedented rates, compromising their surrounding landscapes, and consuming many scarce resources. As a consequence, this will increase the compactness of cities and will also decrease the availability of urban green space. In recent years, many Dutch municipalities have cut back on municipal green space and itsmaintenance. To offer a liveable environment in 30 to 50 years, cities must face challenges head-on and strive to create green urban areas that build on liveable and coherent sustainable circular subsystems.
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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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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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Renewable energy sources have an intermittent character that does not necessarily match energy demand. Such imbalances tend to increase system cost as they require mitigation measures and this is undesirable when available resources should be focused on increasing renewable energy supply. Matching supply and demand should therefore be inherent to early stages of system design, to avoid mismatch costs to the greatest extent possible and we need guidelines for that. This paper delivers such guidelines by exploring design of hybrid wind and solar energy and unusual large solar installation angles. The hybrid wind and solar energy supply and energy demand is studied with an analytical analysis of average monthly energy yields in The Netherlands, Spain and Britain, capacity factor statistics and a dynamic energy supply simulation. The analytical focus in this paper differs from that found in literature, where analyses entirely rely on simulations. Additionally, the seasonal energy yield profile of solar energy at large installation angles is studied with the web application PVGIS and an hourly simulation of the energy yield, based on the Perez model. In Europe, the energy yield of solar PV peaks during the summer months and the energy yield of wind turbines is highest during the winter months. As a consequence, three basic hybrid supply profiles, based on three different mix ratios of wind to solar PV, can be differentiated: a heating profile with high monthly energy yield during the winter months, a flat or baseload profile and a cooling profile with high monthly energy yield during the summer months. It is shown that the baseload profile in The Netherlands is achieved at a ratio of wind to solar energy yield and power of respectively Ew/Es = 1.7 and Pw/Ps = 0.6. The baseload ratio for Spain and Britain is comparable because of similar seasonal weather patterns, so that this baseload ratio is likely comparable for other European countries too. In addition to the seasonal benefits, the hybrid mix is also ideal for the short-term as wind and solar PV adds up to a total that has fewer energy supply flaws and peaks than with each energy source individually and it is shown that they are seldom (3%) both at rated power. This allows them to share one cable, allowing “cable pooling”, with curtailment to -for example-manage cable capacity. A dynamic simulation with the baseload mix supply and a flat demand reveals that a 100% and 75% yearly energy match cause a curtailment loss of respectively 6% and 1%. Curtailment losses of the baseload mix are thereby shown to be small. Tuning of the energy supply of solar panels separately is also possible. Compared to standard 40◦ slope in The Netherlands, facade panels have smaller yield during the summer months, but almost equal yield during the rest of the year, so that the total yield adds up to 72% of standard 40◦ slope panels. Additionally, an hourly energy yield simulation reveals that: façade (90◦) and 60◦ slope panels with an inverter rated at respectively 50% and 65% Wp, produce 95% of the maximum energy yield at that slope. The flatter seasonal yield profile of “large slope panels” together with decreased peak power fits Dutch demand and grid capacity more effectively.
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This booklet holds a collection of drawings, maps, schemes, collages, artistic impressions etc. which were made by students during an intense design moment in the project (re)CYCLE Limburg, which took place in December 2016. Students of Built Environment, Facility Management, Social Work and Health & Care cooperated in making designs and developing strategies for urban renewal in Kerkrade West (Province of Limburg, the Netherlands). The study focused on the importance of qualitative and shared public spaces. The local community (inhabitants, shopkeepers, entrepreneurs, municipality, housing corporation) was actively engaged by sharing knowledge and information, ideas and opinions. These reflections are part of the Limburg Action Lab (part of the Smart Urban Redesign Research Centre). It engages in research by design on innovative and tactical interventions in public space, that might enhance the identity, sustainability and socio-spatial structure of neighbourhoods.
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It is of utmost importance to collect organic waste from households as a separate waste stream. If collected separately, it could be used optimally to produce compost and biogas, it would not pollute fractions of materials that can be recovered from residual waste streams and it would not deteriorate the quality of some materials in residual waste (e.g. paper). In rural areas with separate organic waste collection systems, large quantities of organic waste are recovered. However, in the larger cities, only a small fraction of organic waste is recovered. In general, citizens dot not have space to store organic waste without nuisances of smell and/or flies. As this has been the cause of low organic waste collection rates, collection schemes have been cut, which created a further negative impact. Hence, additional efforts are required. There are some options to improve the organic waste recovery within the current system. Collection schemes might be improved, waste containers might be adapted to better suit the needs, and additional underground organic waste containers might be installed in residential neighbourhoods. There are persistent stories that separate organic waste collection makes no sense as the collectors just mix all municipal solid waste after collection, and incinerate it. Such stories might be fuelled by the practice that batches of contaminated organic waste are indeed incinerated. Trust in the system is important. Food waste is often regarded as unrein. Users might hate to store food waste in their kitchen that could attract insects, or the household pets. Hence, there is a challenge for socio-psychological research. This might also be supported by technology, e.g. organic waste storage devices and measures to improve waste separation in apartment buildings, such as separate chutes for waste fractions. Several cities have experimented with systems that collect organic wastes by the sewage system. By using a grinder, kitchen waste can be flushed into the sewage system, which in general produces biogas by the fermentation of sewage sludge. This is only a good option if the sewage is separated from the city drainage system, otherwise it might create water pollution. Another option might be to use grinders, that store the organic waste in a tank. This tank could be emptied regularly by a collection truck. Clearly, the preferred option depends on local conditions and culture. Besides, the density of the area, the type of sewage system and its biogas production, and the facilities that are already in place for organic waste collection are important parameters. In the paper, we will discuss the costs and benefits of future organic waste options and by discussing The Hague as an example.
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Demand Conference Lancaster 2016Workshop 9. Space, site and scale in the making of energy demandAbstract: Energy scripts and spacesTineke van der Schoor, Hanze University of Applied Sciences, GroningenTechnology is infused with scripts that indicate how we as users should behave around, live in or use an artefact. Drawing inspiration from literature discussing user scripts and gender scripts, we develop the notion of energy scripts. We tentatively define the concept of energy scripts as the way the distribution of light, heat and power within a building choreographs its functional use and stimulates or discourages energy use. We apply this concept to buildings, to analyse if and how the energy demand of buildings is choreographed by architectural design.In the literature, scripts are also investigated in a normative setting. Designers, such as engineers or architects, embed their worldviews into artefacts thus providing an opportunity where scripts ‘materialize morality’. However, users are not necessarily the passive receptacles of these embedded scripts; they have opportunities to ignore, resist or even redesign built artefacts.In European architectural design, it can be seen in the middle ages that the situation of rooms was such that important functions –such as writing manuscripts - could profit from heat produced in the kitchen. Moreover, the distribution of warmth followed the division of labour and class lines, as servant’s quarters were unheated. Modern houses also have energy-scripts, for example kitchens are designed for housing separate appliances, instead of using cool storage. The use of technology for heating and lighting is ubiquitous in modern buildings, while the need to reduce energy demand often leads to the installation of even more technology, smart or otherwise. On the other hand, ‘passive design’ demonstrates that it isquite possible to design buildings that need almost no energy for heating. Furthermore, new ‘daylighting’ technologies bring natural light to the darkest spaces. Historically, there have been ‘paths not taken’, which could have led to a less energy demanding built environment. Retracing these paths can lead to new perspectives on building design and retrofit.Researching the concept of energy-scripts we contribute to our understanding of the constraints and flexibilities for reduced energy demand in buildings. Our approach also sheds light on the social construction of the ‘resident’ or ’house consumer’ as an end-user. Investigating implicit expectations regarding energy use, which could ultimately assist in designing building scripts that specifically invite energy efficient use of a building.
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