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 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.
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TU Delft, in collaboration with Gravity Energy BV, has conducted a feasibility study on harvesting electric energy from wind and vibrations using a wobbling triboelectric nanogenerator (WTENG). Unlike conventional wind turbines, the WTENG converts wind/vibration energy into contact-separation events through a wobbling structure and unbalanced mass. Initial experimental findings demonstrated a peak power density of 1.6 W/m² under optimal conditions. Additionally, the harvester successfully charged a 3.7V lithium-ion battery with over 4.5 μA, illustrated in a self-powered light mast as a practical demonstration in collaboration with TimberLAB. This project aims to advance this research by developing a functioning prototype for public spaces, particularly lanterns, in partnership with TimberLAB and Gravity Energy. The study will explore the potential of triboelectric nanogenerators (TENG) and piezoelectric materials to optimize energy harvesting efficiency and power output. Specifically, the project will focus on improving the WTENG's output power for practical applications by optimizing parameters such as electrode dimensions and contact-separation quality. It will also explore cost-effective, commercially available materials and best fabrication/assembly strategies to simplify scalability for different length scales and power outputs. The research will proceed with the following steps: Design and Prototype Development: Create a prototype WTENG to evaluate energy harvesting efficiency and the quantity of energy harvested. A hybrid of TENG and piezoelectric materials will be designed and assessed. Optimization: Refine the system's design by considering the scaling effect and combinations of TENG-piezoelectric materials, focusing on maximizing energy efficiency (power output). This includes exploring size effects and optimal dimensions. Real-World Application Demonstration: Assess the optimized system's potential to power lanterns in close collaboration with TimberLAB, DVC Groep BV and Gravity Energy. Identify key parameters affecting the efficiency of WTENG technology and propose a roadmap for its exploitation in other applications such as public space lighting and charging.
Als gevolg van de energietransitie wordt het steeds moeilijker om energieaanbod en -vraag op elkaar af te stemmen en ontstaan problemen op het elektriciteitsnet. Energieopslag biedt een oplossing: duurzame energie wordt opgeslagen op momenten dat er aanbod en weinig energievraag is en beschikbaar gesteld wanneer er weinig aanbod en veel vraag is. Lokale opslag biedt een kans om lokale uitval van het elektriciteitsnet te voorkomen en geeft meerwaarde aan duurzame energie. Opslag in waterstof is uitermate geschikt voor zowel toepassingen op MW-schaal (windparken), voor seizoensopslag en voor toepassingen waar distributie relevant is. De wens van bedrijventerreinen om te verduurzamen biedt een kans om gericht aan oplossingen voor lokale energieopslag in waterstof en bijbehorende toepassingen te werken. In dit project werkt de HAN samen met MKB-bedrijven, Saxion, TU Delft, lokale overheden en een aantal overige partners aan het ontwikkelen en optimaliseren van een energieopslagsysteem gebaseerd op waterstof en bijbehorende waterstoftoepassingen op en voor bedrijventerrein IPKW in Arnhem. Beschikbare windenergie van in aanbouw zijnde turbines langs de Rijn bij IPKW vormen de aanleiding voor het ontwerpen, modelleren, construeren en testen van een (geschaald) energieopslagsysteem gebaseerd op de productie, en opslag van waterstof. Specifieke toepassingen op het industriepark worden geïnventariseerd, en waar mogelijk gerealiseerd en gemonitord, voor met name lokaal bedrijfstransport en elektriciteitslevering. Scenario’s voor ontwikkeling en toepassing van de technologie ontwikkeld en haalbaarheidsstudies uitgevoerd. Kennis en expertise worden ontwikkeld om het proces van optimale implementatie van waterstof voor energieopslag in een energieketen met specifieke toepassingen op een bedrijventerrein te ondersteunen. Met dit project bouwen wij voort op de vele eerdere waterstofprojecten die bij de HAN zijn uitgevoerd en maken we gebruik van ons recent gerealiseerde shared facility HAN Waterstoflab op IPKW.
