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.
A transition of today’s energy system towards renewableresources, requires solutions to match renewable energy generationwith demand over time. These solutions include smartgrids, demand-side management and energy storage. Energycan be stored during moments of overproduction of renewableenergy and used from the storage during moments ofinsufficient production. Allocation in real time of generatedenergy towards controlled appliances or storage chargers, isdone by a smart control system which makes decisions basedon predictions (of upcoming generation and demand) andinformation of the actual condition of storages.
MULTIFILE
Renewable energy is often suggested as a possible solution for reducing greenhouse gas emissions and decreasing dependency on fossil energy sources. The most readily available renewable energy sources in Europe, wind, solar and biomass are dispersed by nature, making them ideally suited for use within Decentralized Energy Systems. Decentralized energy grids can help integrate renewable production, short lived by-products e.g. heat, minimize transport of energy carriers and fuel sources and reduce the dependency on fossils, hence, possibly improving the overall efficiency and sustainability of the energy distribution system. Within these grids balance between local renewable production and local energy demand is an important subject. Currently, fluctuations between demand and production of energy are mainly balanced by input from conventional power stations, which operate on storable fossil energy sources e.g. coal, oil, natural gas and nuclear. Within the long term scope of transition towards a low carbon intensive energy system, sustainable systems must be found which can replace fossil energy sources as load balancer in our energy supply systems.
Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
The Dutch floriculture is globally leading, and its products, knowledge and skills are important export products. New challenges in the European research agenda include sustainable use of raw materials such as fertilizer, water and energy, and limiting the use of pesticides. Greenhouse growers however have little control over crop growth conditions in the greenhouse at individual plant level. The purpose of this project, ‘HiPerGreen’, is to provide greenhouse owners with new methods to monitor the crop growth conditions in their greenhouse at plant level, compare the measured growth conditions and the measured growth with expected conditions and expected growth, to point out areas with deviations, recommend counter-measures and ultimately to increase their crop yield. The main research question is: How can we gather, process and present greenhouse crop growth parameters over large scale greenhouses in an economical way and ultimately improve crop yield? To provide an answer to this question, a team of university researchers and companies will cooperate in this applied research project to cover several different fields of expertise The application target is floriculture: the production of ornamental pot plants and cut flowers. Participating companies are engaged in the cultivation of pot plans, flowers and suppliers of greenhouse technology. Most of the parties fall in the SME (MKB) category, in line with the RAAK MKB objectives.Finally, the Demokwekerij and Hortipoint (the publisher of the international newsletter on floriculture) are closely involved. The project will develop new knowledge for a smart and rugged data infrastructure for growth monitoring and growth modeling in the greenhouse. In total the project will involve approximately 12 (teacher) researchers from the universities and about 60 students, who will work in the form of internships and undergraduate studies of interesting questions directly from the participating companies.
Power Quality, ofwel de kwaliteit van spanning en stroom, is momenteel een veelbesproken onderwerp. Door de sterke toename van niet-lineaire en energiebesparende belastingen (denk bijv. aan spaar- en ledverlichting, computervoedingen, frequentieregelaars, solaromvormers, etc.) verslechtert de kwaliteit van de netspanning terwijl diezelfde apparatuur juist gevoeliger worden voor verstoringen. Dit heeft nadelige economische en technische gevolgen voor de levensduur, efficiëntie, betrouwbaarheid en veiligheid van zowel de energie infrastructuur als de aangesloten apparatuur. Het belang van Power Quality blijkt ook uit het recent aantal publicaties en conferenties op dit vakgebied. Desondanks is de technische en wetenschappelijke analyse van Power Quality problemen voornamelijk fenomenologisch van aard. Problemen worden doorgaans beschreven aan de hand van metingen. Oplossingen worden meestal gezocht in het ad hoc plaatsen van commerciële power conditioners die de spanning en stroom beogen te verbeteren. De Power Quality problemen waar MKB-er Kanters al jaren mee worstelt zijn typerend voor de vele Power Quality problemen waar MKB-er HyTEPS, marktleider in Power Quality en Energy Efficiency, dagelijks mee te maken heeft. De installaties kunnen worden doorgemeten, maar het blijft lastig om de veroorzaker(s) van het Power Quality probleem met zekerheid vast te stellen. Het is meestal niet toegestaan om ‘verdachte’ apparaten af te schakelen in dit proces. De optimale plaatsing van de power conditioner(s) blijft daarmee een open vraag. Derhalve ontstaat de dringende behoefte aan computersimulaties om de oorzaak van verstoring te analyseren en de mogelijke oplossingen te valideren. PQsim onderzoekt of middels modellering en simulatie de bron van Power Quality problemen kan worden gealloceerd zodat er efficiënt oplossingen kunnen worden ontwikkeld en toegepast. De kennisassimilatie tussen HyTEPS, Kanters, RUG en de HAN beoogt een solide basis te vormen voor een unieke systematische en regeltechnische benadering van Power Quality problemen. De verworven inzichten dienen voorts als input voor toekomstige SiA-RAAK/TKI projecten.
