Zoekresultaten

Qualified labour for Groningen Energy Port

Third chapter of the English version of the book 'Energieke Arbeid' published by the Centre of Applied Labour Market Research and Innovation (Dutch abbreviation: KCA) to celebrate the 10th anniversary of applied labour market research at Hanze University of Applied Sciences. This chapter discusses the second line of research of KCA: The Labour Market in the EnergyPort Groningen Region.

Flexines’ role model extensions for the smart energy market

In this paper we focus on the inclusion of decentralized energy production in the current energy infrastructure, and the changing role of the consumer towards a producer and an active participant in the energy value network.

Praktijkgericht onderzoek voor urban energy

Uit de samenvatting: "Sinds medio 2017 is het Nationaal Lectorenplatform Urban Energy actief. De betrokken lectoren beogen het praktijkgericht onderzoek rond de gebouwde omgeving op hogescholen te verbinden en te stroomlijnen. Dit doen ze teneinde bij te dragen aan de energietransitie: met duurzame bronnen voorzien in onze energievoorziening. Een belangrijk instrument om de expertise van de lectoren te delen is een digitale onderzoekskaart, die beschikbaar is via: http://www.nlurbanenergy.nl. Daarnaast is er behoefte aan meer inzicht als het gaat om termen als vraagarticulatie en onderzoekssamenwerking. Meer precies wilden we achterhalen wat de behoefte is van het mkb aan praktijkgericht onderzoek van hogescholen in het domein Urban Energy. Daartoe hebben we een verkennende studie uitgevoerd naar praktijkgericht onderzoek binnen het domein Urban Energy. Hiervoor interviewden we de betrokken lectoren en ondernemers uit het innovatief MKB. Daarnaast maakten we gebruik van een enquête die we via verschillende kanalen onder de aandacht brachten bij het innovatief mkb."

Building and building systems energy prediction models to enhance energy flexibility control

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.

Open innovation as EnTranCe to the energy market

Energy storage label

This report describes the creation and use of a database for energy storage technologies which was developed in conjunction with Netbeheer Nederland and the Hanze University of Applied Sciences. This database can be used to make comparisons between a selection of storage technologies and will provide a method for ranking energy storage technology suitability based on the desired application requirements. In addition, this document describes the creation of the energy storage label which contains detailed characteristics for specific storage systems. The layout of the storage labels enables the analysis of different storage technologies in a comprehensive, understandable and comparative manner. A sampling of storage technology labels are stored in an excel spreadsheet and are also compiled in Appendix I of this report; the storage technologies represented here were found to be well suited to enable flexibility in energy supply and to potentially provide support for renewable energy integration [37] [36]. The data in the labels is presented on a series of graphs to allow comparisons of the technologies. Finally, the use and limitations of energy storage technologies are discussed. The results of this research can be used to support the Dutch enewable Energy Transition by providing important information regarding energy storage in both technically detailed and general terms. This information can be useful for energy market parties in order to analyze the role of storage in future energy scenarios and to develop appropriate strategies to ensure energy supply.