Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive. Link to the formal publication via its DOI https://doi.org/10.1016/j.enbuild.2020.110289
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.
The future energy system could benefit from the integration of independent gas, heat and electricity infrastructures. Such a hybrid energy network could support the increase of intermittent renewable energy sources by offering increased operational flexibility. Nowadays, the expectations on Natural Gas resources forecast an increase in the application of Liquefied Natural Gas (LNG), as a means of storage and transportation, which has a high exergy value. Therefore, we analyzed the integration of decentralized LNG regasification with a Waste-to-Energy (W2E) plant for a practice-based case to get an idea on how it might affect the balancing of supply and demand, under optimized exergy efficient conditions. We compared an independent system with an integrated system that consists of the use of the LNG cold to cool the condenser of the W2E plant, as well as the expansion of the regasified LNG in an expander, using a simplified deterministic model based on the energy hub concept. We use the hourly measured electricity and heat demand patterns for 200 households with 35% of the households producing electricity from PV according to a typical measured solar insolation pattern in The Netherlands. The results indicate that the integration affects the imbalance for electricity and heat compared to the independent system. If the electricity demand is met, both the total yearly heat shortage and heat excess are reduced for the integrated system. If the heat demand is met, the total yearly electricity shortage is also reduced (with 100 MWh). However, the total yearly electricity excess is then increased (with 300 MWh). We observed that these changes are solely due to the increase in exergy efficiencies for heat and electricity of the W2E Rankine cycle. The efficiency of the expander is too low to offer a significant contribution to the electricity demand. Therefore, future research should focus on the affect that can be obtained by to other means of integration (e.g. Organic Rankine Cycle and Stirling Cycle).
This Professional Doctorate (PD) research focuses on optimizing the intermittency of CO₂-free hydrogen production using Proton Exchange Membrane (PEM) and Anion Exchange Membrane (AEM) electrolysis. The project addresses challenges arising from fluctuating renewable energy inputs, which impact system efficiency, degradation, and overall cost-effectiveness. The study aims to develop innovative control strategies and system optimizations to mitigate efficiency losses and extend the electrolyzer lifespan. By integrating dynamic modeling, lab-scale testing at HAN University’s H2Lab, and real-world validation with industry partners (Fluidwell and HyET E-Trol), the project seeks to enhance electrolyzer performance under intermittent conditions. Key areas of investigation include minimizing start-up and shutdown losses, reducing degradation effects, and optimizing power allocation for improved economic viability. Beyond technological advancements, the research contributes to workforce development by integrating new knowledge into educational programs, bridging the gap between research, industry, and education. It supports the broader transition to a CO₂-free energy system by ensuring professionals are equipped with the necessary skills. Aligned with national and European sustainability goals, the project promotes decentralized hydrogen production and strengthens the link between academia and industry. Through a combination of theoretical modeling, experimental validation, and industrial collaboration, this research aims to lower the cost of green hydrogen and accelerate its large-scale adoption.
