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.
Gepubliceerd in Mikroniek, nr. 6 2018 In manufacturing environments where collaborative robots are employed, conventional computer vision algorithms have trouble in the robust localisation and detection of products due to changing illumination conditions and shadows caused by a human sharing the workspace with the robotic system. In order to enhance the robustness of vision applications, machine learning with neural networks is explored. The performance of machine-learning algorithms versus conventional computer vision algorithms is studied by observing a generic user scenario for the manufacturing process: the assembly of a product by localisation, identification and manipulation of building blocks.
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Purpose The purpose of this paper is to synthesize the fragmented literature on organizational citizenship behavior (OCB), leader–member exchange (LMX), learning, innovative work behavior (IWB) and employee performance across different countries, disciplines and organizations, thereby broadening the literature breath and making gap identification comprehensive. Second, it provides information on how much studies have been concentrated on Africa with the goal of provoking scholarly work in a unique cultural setting on the interrelatedness of these concepts. Design/methodology/approach Relevant literature search was undertaken using key search terms, “employee performance,” “OCB,” “LMX,” “IWB,” “individual learning” and “team learning.” Findings The findings show positive relationships between the behaviors and employee performance. They also reveal an interesting diversity in the study across multidisciplinary fields holding both cultural and contextual significance for academia and practitioners. Research limitations/implications – The limitation of literature to peer-reviewed journals from the authors’ university library might have missed important information not in this domain. Further studies must make use of additional search terms and engines excluded from this study to provide a more comprehensive analysis. Practical implications The paper has important managerial implications for practitioners. The analysis can support the understanding of employee performance from a broader and more diverse view points; and help in providing insight into real-life opportunities, constraints and solutions in enhancing performance management. Originality/value – This systematic literature review highlights important knowledge gaps which need to be explored especially in the African and Ghanaian contexts.
In the past, textile material was used to add value to buildings in various applications, as well as improving building performance in terms or in terms of building and acoustics properties, and increasing the esthetic value.Textiles are light in weight, easy to shape, strong, insulating, moisture-regulating and can be provided with extra functions. Particularly in areas with an earthquake risk, as well as cases with a temporary demand for flexible shelters, textiles and primary use.
The energy transition is a highly complex technical and societal challenge, coping with e.g. existing ownership situations, intrusive retrofit measures, slow decision-making processes and uneven value distribution. Large scale retrofitting activities insulating multiple buildings at once is urgently needed to reach the climate targets but the decision-making of retrofitting in buildings with shared ownership is challenging. Each owner is accountable for his own energy bill (and footprint), giving a limited action scope. This has led to a fragmented response to the energy retrofitting challenge with negligible levels of building energy efficiency improvements conducted by multiple actors. Aggregating the energy design process on a building level would allow more systemic decisions to happen and offer the access to alternative types of funding for owners. “Collect Your Retrofits” intends to design a generic and collective retrofit approach in the challenging context of monumental areas. As there are no standardised approaches to conduct historical building energy retrofits, solutions are tailor-made, making the process expensive and unattractive for owners. The project will develop this approach under real conditions of two communities: a self-organised “woongroep” and a “VvE” in the historic centre of Amsterdam. Retrofit designs will be identified based on energy performance, carbon emissions, comfort and costs so that a prioritisation strategy can be drawn. Instead of each owner investing into their own energy retrofitting, the neighbourhood will invest into the most impactful measures and ensure that the generated economic value is retained locally in order to make further sustainable investments and thus accelerating the transition of the area to a CO2-neutral environment.
The Northern Netherlands (NN) finds itself at the junction of all the big transitions. Digitalisation is essential to follow through with these. Considering this, our region has the potential to make sizeable progress if it can successfully roll out widespread digitalisation. As a hardcore transition economy, the NN may even join the European frontrunners and act as an example for other regions. It is from this challenge that the NN will start with the European Digital Innovation Hub (EDIH NN). We have chosen to specialise in the area of Autonomous Systems, which includes multiple digital technologies that are relevant for the four transitions in the NN: (1) Smart Agro, (2) Smart Manufacturing, (3) Life Science and Health and (4) Utilities, Built Environment and Mobility. In the first three-year EDIH NN wants to support more than 750 companies and lay the foundation for long-term support of all companies. The following building blocks for EDIH NN are: • A Brokerage network that will identify issues regarding digitalisation and relay these to Solution Providers (high TRL) and knowledge providers (low TRL). • A Test Before Invest network (test and demo facilities) comprising 20+ organisations that will invest in Autonomous Systems within their domain, and collaborate towards becoming a European testing ground. • A Smart Factory Accelerator to strengthen the digital maturity of companies. • An Empowerment programme to strengthen companies in the areas of DEP Technologies: Cyber Security and Artificial Intelligence. • An approach based on High Performance Computing to make digitalisation more accessible. • The Smart Makers Academy: A programme aimed at matching supply and demand around digital skills, based on individual learning outcomes. • A Funding Readiness programme to help companies that need to invest for their digitalisation strategy, in finding funding opportunities. • A network to stimulate supply and demand around Autonomous Systems
