In this paper we present a methodology to measure the energy consumption of software. The methodology is based on detailed monitoring of power usage of hardware components. We explain our lab setup after which we apply the methodology to different pieces of DNS resolver software. Through this case study we demonstrate some of the uses of our methodology, as it can be used to determine which software performs the tasks at hand in the most energy efficient way, what the influence of software configuration can be, etcetera.
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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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Environmental or ‘green' education is an important driving force behind the ‘greening' of society as it plays a critical role in raising environmental awareness and preparing students for green jobs. None of the existing environmental attitudes and behavior measures is focused on the evaluation of green education, especially in relation to consumption. To date, no longitudinal studies of children and students' attitudes towards consumption influenced by education exist. Also, little has been done to explore the socio-cultural context in which attitudes toward consumption are being formed and to explain the cross-cultural differences in environmental attitudes. This pilot study is designed to take the first step towards developing methods complementing existing quantitative measurements with qualitative strategies, such as consumption diaries, focus groups, and concept mapping. While this research is just a first attempt to tackle children's knowledge and attitudes consumption, preliminary results of the research on which this chapter is based and enthusiasm of the research participants encourage the author to stress the importance of consumption studies as part of green education for educational program developers. As a chapter of this volume, the author hopes that this study will add to the anthropological depository of research on the cultural variants in the perception of the environment in children. This chapter draws upon the consumption diaries collected from the upper-elementary school children in Amsterdam, The Netherlands, between September 2009 and May 2010. Consumption diaries are chronological documents recording purchase, use, and waste of materials, which can be used both as analytical tools and the means to stimulate environmental awareness. The four main methodological steps involved in this research were as follows. Children were asked to complete the consumption diary, paying specific attention to use and waste materials. Consequently, focus group meetings were held with parents and their children to discuss the diaries. Finally, interviews with the children were conducted in order to generate statements that supplement those generated by focus groups for carrying out the concept mapping analysis. The concept mapping analysis was then conducted to organize the order and analyze the ideas expressed in the focus group and interview sessions. This is an Accepted Manuscript of a book chapter published by Routledge/CRC Press in "Environmental Anthropology Today" on 8/5/11 available online: https://doi.org/10.4324/9780203806906 LinkedIn: https://www.linkedin.com/in/helenkopnina/
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Aanleiding: Automatisering kan leiden tot beter gebruik van materialen en afval reduceren. Dit brengt verbeteringen met zich mee voor 'people, planet and profit' (PPP) - mensen, het milieu en de winst. Een specifieke vorm van automatisering, de ontwikkeling van zelfrijdende auto's en vrachtauto's, gaat snel. Maar om zelfrijdende voertuigen beschikbaar te maken is er nog veel onderzoek en ontwikkeling nodig op verschillende gebieden. Er zijn nog veel vragen te beantwoorden op het gebied van onder andere truckontwerp, betrouwbare software, aansprakelijkheid, trajectplanning en logistiek. Doelstelling Het doel van het Intralog-project is om voor de maatschappij en de private sector een significante bijdrage te leveren aan de mogelijkheden van zelfrijdende voertuigen in de commerciële transportsector. Het Intralog-project onderzoekt de toegevoegde waarde voor PPP van 'automated guided trucks' (AGT's) aan logistieke operaties bij distributiecentra en interterminal/intermodal traffic hubs. Dit gebeurt in twee stappen: 1) het identificeren van het potentieel met betrekking tot de vraag vanuit de logistieke omgeving; 2. het ontwerpen, realiseren, testen en valideren van mogelijke strategieën voor het implementeren van AGT's in een logistiek scenario. Beoogde resultaten Het concrete resultaat van het project bestaat uit onderzoekstools en hardware- en softwaremodellen voor Intralog. Deze bieden een goede mogelijkheid om de opgedane kennis te verspreiden. De projectdeelnemers zullen bijdragen aan workshops, tentoonstellingen en in Nederland georganiseerde symposia. De onderzoeksresultaten verspreiden ze op conferenties en door middel van publicaties in technische vakbladen. De uiteindelijke Intralog-resultaten worden gepresenteerd op een afsluitend congres. De resultaten zullen worden samengevat in een boekje.
