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Dietary protein intake in Dutch elderly people

INTRODUCTION: Sufficient high quality dietary protein intake is required to prevent or treat sarcopenia in elderly people. Therefore, the intake of specific protein sources as well as their timing of intake are important to improve dietary protein intake in elderly people.OBJECTIVES: to assess the consumption of protein sources as well as the distribution of protein sources over the day in community-dwelling, frail and institutionalized elderly people.METHODS: Habitual dietary intake was evaluated using 2- and 3-day food records collected from various studies involving 739 community-dwelling, 321 frail and 219 institutionalized elderly people.RESULTS: Daily protein intake averaged 71 ± 18 g/day in community-dwelling, 71 ± 20 g/day in frail and 58 ± 16 g/day in institutionalized elderly people and accounted for 16% ± 3%, 16% ± 3% and 17% ± 3% of their energy intake, respectively. Dietary protein intake ranged from 10 to 12 g at breakfast, 15 to 23 g at lunch and 24 to 31 g at dinner contributing together over 80% of daily protein intake. The majority of dietary protein consumed originated from animal sources (≥60%) with meat and dairy as dominant sources. Thus, 40% of the protein intake in community-dwelling, 37% in frail and 29% in institutionalized elderly originated from plant based protein sources with bread as the principle source. Plant based proteins contributed for >50% of protein intake at breakfast and between 34% and 37% at lunch, with bread as the main source. During dinner, >70% of the protein intake originated from animal protein, with meat as the dominant source.CONCLUSION: Daily protein intake in these older populations is mainly (>80%) provided by the three main meals, with most protein consumed during dinner. More than 60% of daily protein intake consumed is of animal origin, with plant based protein sources representing nearly 40% of total protein consumed. During dinner, >70% of the protein intake originated from animal protein, while during breakfast and lunch a large proportion of protein is derived from plant based protein sources.

Trends and developments in the renewable energy sector in Ukraine

The government of Ukraine has adopted the Renewable Energy Directive (RED) with clear goals and a roadmap to facilitate its energy transition towards renewable sources. This is done because of both climate concerns as well as reasons related to Ukraine’s foreign policy which led the government to decide that Ukraine should work more on its own energy independence. Currently the percentage of renewable energy sources in Ukraine is among the lowest of the entire Europe and there is only slow development in terms of the growth of the sector, even though there is a lot of available biomass, given the large and flat surface of the country with a well-developed agricultural sector. As in most countries in the world, there is a quite intensive and well-developed debate in Ukraine about the energy sector, energy usage and the necessary transition towards more renewable types of energy. One of the consequences of it is that Ukraine is one of the partner countries in the Paris agreement and committed itself to reducing the amount of greenhouse gas emissions in the future. That means that a transformation towards renewable energy is needed, even though currently in Ukraine only a low percentage of energy is generated by sustainable sources. The general picture is that in Ukraine the development of the renewable energy sector is going not as fast as could have been. In other words, there are several barriers present that hinder the energy transition. One of the issues behind such a barrier may be a limited access to technology, or problems with legislation or other issues which may be unknown so far, but certainly relevant for foreign investors. The Ukrainian government adopted the so-called Renewable Energy Directive (RED), set goals for the energy transition and support the transition itself. In some areas progress was made, for example in the growing number of biomass fired boilers, but still Ukraine remains one of the European countries with the lowest percentage of renewable energy production. Therefore, in order to identify currently existing barriers and help to find possible applications of new technologies in Ukraine, the Dutch Enterprise Agency (Rijksdienst voor Ondernemerschap) commissioned this study. It was done within the framework of the Partners in Business on Bioenergy program. The focus of this study is on analysing the renewable energy sector, with special attention for biomass, in the form of biomass-based heating and biomass for biofuels. Of course, other parts of the renewable energy sector such as solar and wind energy are also taken into consideration. The second part consists of a case study to determine the business case for direct processing of sugar beets with Betaprocess as a possible application of biomass to biofuel production in Ukraine. The third study is aiming at determining the amount of biomass that can safely be taken from the fields, without negatively affecting the fertility of the soil. These sub-studies mentioned in the previous paragraph offer a better understanding of the renewable energy market in general and biomass/biofuel applications in particular. This study sheds light on several important questions that entrepreneurs and/or other foreign investors may have about investing in Ukraine. Even though it is well-known that doing business in Ukraine is challenging, it is also very important to have a clear picture of the opportunities that this country offers, within the limits that nature sets, in order to avoid negative consequences like soil degradation. The objective of this report is to find out about which opportunities and barriers exist in the Ukrainian transition towards renewable energy generation, to calculate the profitability of new biomass-processing technologies as well as finding out limitations of biomass usage.

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.

WE ENERGY GAME

Sustainability has become an important blueprint to achieve a better future for all, and as part of this process, nations are called to accelerate an energy transition towards clean energy solutions. However, an often-neglected pillar is educating individuals on the benefits and challenges of energy efficiency and renewable energy, especially among young people. Their support and willingness to use clean energies will be a significant driver in short, medium and long term. However, reality shows that attention from youth on these issues has not been sufficient yet. Formal education settings become therefore a key place to educate youth in the energy transition. In search of innovative approaches, game-based learning is gaining popularity among scholars and practitioners; it can contribute to content development of complex issues by integrating insights from different disciplines in an interactive, fun and engaging manner.In this context, we would like to present “the We-Energy Game” as an innovative educational strategy which makes use of game-based learning to create understanding on the challenges in the provision of affordable energy from renewable sources for an entire town. During the game, players negotiate, from their respective roles, which energy source they want to employ and on which location, with the goal to make a village or city energy neutral. The game has been played by students in higher education institutions in The Netherlands.In addition to introducing the game, a study is presented on the effects of the game on students´ awareness on the energy transition, self-efficacy -the feeling that they can contribute to a sustainable energy transition in their towns by themselves- and collective efficacy -the feeling that they can contribute to a sustainable energy transition in their towns together with their community-. For that purpose, we conducted a survey with 100 bachelor (Dutch and international) students aged between 18 and 30 years old, at Hanze University of Applied Sciences, before and after playing the game. We also conducted a group discussion with a smaller group of students to understand their opinion about the game. From the survey, results reveal an increase in awareness about the energy transition, as well as (slightly higher) collective efficacy compared to self-efficacy. From the group discussion, findings reveal that the game makes students reflect on the complexity of the process and need for collaboration among different stakeholders. It also shows how educational games have still a long way to go to achieve the high levels of engagement of commercial games, despite the fact that students still preferred to have this type of interactive practice rather than a traditional class characterized by a unidirectional transmission of information. Different implications must be taken into account for educators when interested in implementing game-based learning in class, including immediate feedback, appropriate length of gameplay during class, and time for a reflection and critical thinking after playing the game.

The role of perceived contribution to the energy transition in acceptance of renewable energy sources

Videoverslag RAAK Onderzoek 'Making GREEN Energy Sources Greener'

Videoverslag waarin de aanpak, maatschappelijke relevantie en belangrijkste uitkomsten van het RAAK Onderzoek 'Making GREEN Energy Sources Greener' worden besproken. In dit onderzoek is op verschillende drijvende zonneparken gekeken naar effecten van de installaties op waterkwaliteit en ecologie. De resultaten hiervan vormen aanleiding voor vervolgonderzoeken die inmiddels zijn gestart