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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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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Het project “In twee stappen naar een aardgasvrije en comfortabele Nederlandse woonomgeving” ontwikkelt een manier om de overstap naar aardgasvrije woningverwarming eenvoudiger, minder spannend, betrouwbaarder en beter te maken. Dat doen we door een “hybride” tussenstap, die een behoorlijke aardgasreductie en financiële besparing geeft, eenvoudig en relatief goedkoop te realiseren is, zonder onzekerheid over comfort.Eerst stond het ontwikkelen en testen van een lucht-water warmtepomp met volledige binnenopstelling centraal. Als deze ook efficiënt is bij hogere watertemperatuur, kan dezelfde warmtepomp eerst hybride worden ingezet en later volledig de warmtevraag en tapwatervraag overnemen. Bij tests bleek het vermogen echter te beperkt voor doorsnee rijtjeswoningen. Daarom zouden er voor een gasvrije woning 2 warmtepompen nodig zijn. Door eerst een airco (lucht-lucht warmtepomp) te installeren in de hoofdruimte en later de ketel te vervangen door een goed gedimensioneerde lucht-water warmtepomp met buffer, kan de overstap naar gasvrij worden gemaakt op een manier die ons in dit project voor ogen stond.Dit rapport beschrijft dit “airco hybride” concept en vergelijkt dit met een hybride lucht-water warmtepomp en met in één keer de overstap maken met één warmtepomp. De airco is veel goedkoper in aanschaf en installatie, is snel te leveren en installeren, kan koelen, en verwarmt relatief snel. Doordat de al aanwezige radiatoren minder warmte hoeven te leveren, kunnen die werken bij een lagere temperatuur. Dat maakt de lucht-water warmtepomp efficiënter, terwijl aanpassingen in het afgiftesysteem minder noodzakelijk zijn. Omdat de overstap naar een lucht-water warmtepomp pas later komt, kan men de tussentijdse ontwikkelingen benutten.Een integrale regeling is een essentieel onderdeel van het concept. In stap één (airco toevoegen) worden temperaturen en vermogens gemonitord, waardoor in stap twee (vervangen ketel door gasvrije warmtebron) de juiste configuratie kan worden gekozen, de noodzakelijke aanpassingen aan isolatie en afgiftesysteem in beeld worden gebracht, en de energiekosten en netbelasting kunnen worden berekend. Tijdens beide stappen stuurt de regeling de airco en ketel (en later de lucht-water warmtepomp) zodanig aan dat beide efficiënt draaien.Van een 3,5 kW airco is de COP gemeten bij 1,5 – 4,0 kW warmtevraag en buitentemperaturen van -10 tot +12°C. Als de airco niet hoeft te ontdooien is de COP maximaal bij 2 à 2,5 kW warmtevraag. De prestaties zijn dan vergelijkbaar met een monoblock lucht-water warmtepomp die water van 35°C levert voor vloerverwarming. Bij buitentemperatuur onder 3°C daalt de COP en wordt maximaal 2,5 kW vermogen geleverd. Bij 3,5 à 4 kW is de COP 1 à 1,3 lager dan bij 2 kW warmtevraag. De laagste COP werd gemeten bij 1,0 kW warmtevraag. Ontwikkeling van vermogenssturing in combinatie met het cv-systeem is dus de moeite waard. De lagere COP bij hoge vermogens hangt samen met de hoge temperatuur van koudemiddel en uitblaaslucht in de binnenunit. Dit hing samen met de relatief lage luchtstroom en de warmteoverdracht in meestroom. Als airco’s vooral worden ingezet voor verwarming is het de moeite waard om de mogelijke COP verhoging door grotere binnenunits met tegenstroom te onderzoeken. Het regelgedrag is onderzocht bij constante warmtevraag, zowel van een airco alleen als in combinatie met een aan-uit geschakelde ketel die dezelfde ruimte verwarmt via radiatoren. Ook is het regelgedrag van een airco en ketel onderzocht bij een variabele warmtevraag, waarbij de ketel afzonderlijk werd geregeld door een ruimtethermostaat. De trage reactie van de cv-afgifte leidde tot een variabele ruimtetemperatuur, en maakt dat het airco- vermogen niet goed kan worden gestuurd door de ketel aan/uit te schakelen. In hoofdstuk 5 wordt voorgesteld hoe dit beter zou kunnen, en wat de mogelijke vervolgstappen in het ontwikkelingstraject zouden kunnen zijn.
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