Ecodesign and Energy Label for Household Washing machines and washer dryers

The European Commission launched in 2014 the revision of the ecodesign and energy-/resource label implementing measures for the product group 'household washing machines and household washer dryers (WM-WD)'. The revision study follows the Commission’s Methodology for the Evaluation of Energy related Products (MEErP) consisting of: Scope definition, standard methods and legislation, Market analysis, Analysis of user behaviour and system aspects, Analysis of technologies, Environmental and economics, Design options and Policy analysis and scenarios The comprehensive analysis of the product group following the steps above will feed as research evidence basis into the revision of the existing Energy Label Regulation (EC) 1060/2010 and the Ecodesign Regulation (EC) 1015/2010 on household washing machines. The research is based on available scientific information and data, uses a life-cycle thinking approach, and has engaged stakeholder experts in order to discuss key issues, and to the extent possible reach consensus on the proposals.JRC.B.5-Circular Economy and Industrial Leadershi

Hydrodynamic effects on soiled surfaces : an experimental study and theoretical analysis

This thesis presents the findings of an investigation aimed at understanding the effects of hydrodynamic shear stress on soil removal from textile surfaces both inside the washing machine and in closely controlled laboratory conditions. The research has involved developing a technique for the indirect measurement of shear stress based on pressure recordings and small block probes that provide both an indication of shear stress magnitude and direction. The probes have been investigated and calibrated in a purpose-built, rectangular-section, water tunnel in which tests were also carried out on specially prepared soiled cotton textile samples. This has allowed the correlation between shear stress and soil removal efficiency for 15k ≤ Re ≤ 155k. A more general investigation involving the washing machine has also been carried out so as to quantify and compare the effects of detergency, water temperature, time, abrasion, warping and tangential shear force. The hydrodynamic conditions inside the washing machine were investigated providing insight into flow conditions both on the inside and outside of the wash load. This was achieved through the use of a wireless device in the form of a sphere with an on-board pressure-flow sensor and radio transmitter for remote monitoring via radio. This remote data acquisition system was designed, developed and patented by the author. A model of the wash load motion has also been developed and high speed filming techniques employed to qualify and quantify the wash load dynamics. The main outcome of the research may be summarised as follows: - Hydrodynamic shear stress up to 7.7Pa a circa (15k ≤ Re ≤ 155k) is inadequate to remove soil from the standard EMPA textile surfaces. - Only 10-15% of the soil removed in the washing machines can be attributed to abrasion, warping (due to churning) and hydrodynamic shear stress, the rest is attributed to detergency and heat transfer. - There are at least two different flow domains within a horizontal-axis washing machine, one on the inside of the wash load and one on the outside. - Flow conditions on both the inside and outside are turbulent and velocities up to several meters per second have been recorde

Household water end-use identification in the presence of rudimentary data

Thesis (PhD)--Stellenbosch University, 2021.ENGLISH ABSTRACT: Detailed and accurate information regarding residential water use is essential for targeted water demand management (WDM) strategies and water security, and yet most utilities have limited information regarding household water demand at end-use level. Flow trace analysis software has been successfully deployed to disaggregate household water end-uses from high resolution smart meter data in various earlier studies, however, water utilities from a range of socio-economic settings, especially in developing countries, typically measure household water consumption data at resolutions too low for commercially available disaggregation software. The aim of this research was to identify and develop methods to evaluate and quantify household water demand at an end-use level, in the absence of high resolution data. Numerous end-use studies were conducted using direct methods (i.e. water meters) and indirect methods (e.g. temperature loggers) to record residential water demand at the point of entry and at the point of use. Valuable information was extracted from the recorded time series data by applying the automated temperature analysis algorithm, with end-use event durations and event frequencies being derived from the results. Numerous benefits and limitations regarding temperature loggers as indirect method were addressed as part of this research. Additionally, measurements were taken at a single entry point on a residential property. An automated end-use extraction tool (PEET) and classification model (WEAM) were developed to identify and categorise residential end-use events from a rudimentary data set. Despite the coarse resolution of the measured data making it impossible to separately classify background leakage and relatively low flow water use events (consequently categorising both instances as minor events), PEET was able to extract notable end-use events from the study site. The WEAM model was able to correctly classify the notable end-use events into indoor use and outdoor use categories. The methods and models proposed as part of this research could enable utilities to broadly classify household end-use events as being indoor or outdoor, without relying on pre-trained models. By applying the developed models on rudimentary data sets, water managers could improve water security through better informed demand management programmes.AFRIKAANSE OPSOMMING: Geen opsomming beskikbaarMaster

Contribuitions and developments on nonintrusive load monitoring

Energy efficiency is a key subject in our present world agenda, not only because of greenhouse gas emissions, which contribute to global warming, but also because of possible supply interruptions. In Brazil, energy wastage in the residential market is estimated to be around 15%. Previous studies have indicated that the most savings were achieved with specific appliance, electricity consumption feedback, which caused behavioral changes and encouraged consumers to pursue energy conservation. Nonintrusive Load Monitoring (NILM) is a relatively new term. It aims to disaggregate global consumption at an appliance level, using only a single point of measurement. Various methods have been suggested to infer when appliances are turned on and off, using the analysis of current and voltage aggregated waveforms. Within this context, we aim to provide a methodology for NILM to determine which sets of electrical features and feature extraction rates, obtained from aggregated household data, are essential to preserve equivalent levels of accuracy; thus reducing the amount of data that needs to be transferred to, and stored on, cloud servers. As an addendum to this thesis, a Brazilian appliance dataset, sampled from real appliances, was developed for future NILM developments and research. Beyond that, a low-cost NILM smart meter was developed to encourage consumers to change their habits to more sustainable methods.Eficiência energética é um assunto essencial na agenda mundial. No Brasil, o desperdício de energia no setor residencial é estimado em 15%. Estudos indicaram que maiores ganhos em eficiência são conseguidos quando o usuário recebe as informações de consumo detalhadas por cada aparelho, provocando mudanças comportamentais e incentivando os consumidores na conservação de energia. Monitoramento não intrusivo de cargas (NILM da sigla em inglês) é um termo relativamente novo. A sua finalidade é inferir o consumo de um ambiente até observar os consumos individualizados de cada equipamento utilizando-se de apenas um único ponto de medição. Métodos sofisticados têm sido propostos para inferir quando os aparelhos são ligados e desligados em um ambiente. Dentro deste contexto, este trabalho apresenta uma metodologia para a definição de um conjunto mínimo de características elétricas e sua taxa de extração que reduz a quantidade de dados a serem transmitidos e armazenados em servidores de processamento de dados, preservando níveis equivalentes de acurácia. São utilizadas diferentes técnicas de aprendizado de máquina visando à caracterização e solução do problema. Como adendo ao trabalho, apresenta-se um banco de dados de eletrodomésticos brasileiros, com amostras de equipamentos nacionais para desenvolvimentos futuros em NILM, além de um medidor inteligente de baixo custo para desagregação de cargas, visando tornar o consumo de energia mais sustentável

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

End-of-life implications of electronic textiles - Assessment of a converging technology

Contemporary innovation in the converging technology sectors of electronics and textile aims at augmenting functionality of textiles, making them “smart”. That is, integrating electronic functions such as sensing, data processing, and networking into wearable products. Embedding electronic devices into textiles results in a novel category of products: electronic textiles (e-textiles). Whereas researchers and innovators are pushing forward technological development little attention has been paid to the end-of-life implications of such future products. E-textiles may not only entail promising business opportunities but also adverse environmental impacts. This study examines potential end-of-life implications, which could emerge once future e-textiles are disposed of. Using the methodological framework of technology assessment an overview of current innovation processes for e-textiles is established and an outlook on future applications areas is provided. Further, information on technologies and materials composition of e-textiles is mapped as a basis for assessing the prospective implications at the end of their useful life. The findings suggest that widespread application of e-textiles could result in the emergence of a new waste stream. There are various parallels to electronic waste, which causes profound environmental problems nowadays. Risks include potential release of toxic substances during the disposal phase. And, loss of scarce materials is to be expected if no recycling takes place. This would accelerate the depletion of resources. Recycling of textile integrated electronic devices will be difficult. From the analysis it can be deduced that today’s schemes for takeback, recycling and disposal would not be sufficient to cope with waste e-textiles in an environmentally benign manner. Instead, discarded e-textiles would find their way into solid waste and increase the existing environmental problems of waste disposal. The study concludes with recommendations for policy makers and technology developers on how a waste preventative technology design could be achieved

Building appliances energy performance assessment

Trabalho de Projeto de Mestrado, Informática, 2021, Universidade de Lisboa, Faculdade de CiênciasO consumo de energia tem vindo a crescer na União Europeia todos os anos, sendo de prever que, a curto prazo, se torne insustentável. No sentido de prevenir este cenário, a Comissão Europeia decidiu definir uma Estratégia Energética para a União Europeia, destacando dois objetivos: aumentar a eficiência energética e promover a descarbonização. Atualmente, cerca de 72% dos edifícios existentes na União Europeia não são energeticamente eficientes. Este problema motivou-nos à pesquisa e criação de soluções que permitam uma melhor avaliação do consumo energético por dispositivos elétricos em edifícios residenciais. Neste contexto, o trabalho desenvolvido nesta tese consiste no desenho de uma solução de monitorização remota que recolhe informações de consumo energético recorrendo a técnicas de intrusive load monitoring, onde cada dispositivo elétrico individual é continuamente monitorizado quanto ao seu consumo energético. Esta abordagem permite compreender o consumo de energia, em tempo real e no dia-a-dia. Este conhecimento oferece-nos a capacidade de avaliar as diferenças existentes entre as medições laboratoriais (abordagem utilizada no sistema de rotulagem de equipamentos elétricos de acordo com a sua eficiência energética) e os consumos domésticos estimados. Para tal, nesta tese exploram-se abordagens de machine learning que pretendem descrever padrões de consumo, bem como reconhecer marcas, modelos e que funções os dispositivos elétricos estarão a executar. O principal objetivo deste trabalho é desenhar e implementar um protótipo de uma solução de IoT flexível e de baixo custo para avaliar equipamentos elétricos. Será utilizado um conjunto de sensores que recolherá dados relacionados com o consumo de energia e os entrega à plataforma SATO para serem posteriormente processados. O sistema será usado para monitorar aparelhos comumente encontrados em residências. Além disso, o sistema terá a capacidade de monitorizar o consumo de água de aparelhos que necessitem de abastecimento de água, como máquinas de lavar e de lavar louça. Os dados recolhidos serão usados para classificação dos aparelhos e modos de operação dos mesmos, em tempo real, permitindo fornecer relatórios sobre o consumo energético e modo de uso dos aparelhos, com grande grau de detalhe. Os relatórios podem incluir o uso de energia por vários ciclos de operação. Por exemplo, um aparelho pode executar vários ciclos de operação, como uma máquina de lavar que consume diferentes quantidades de energia elétrica e água consoante o modo de operação escolhido pelo utilizador. Toda a informação recolhida pode ser posteriormente utilizada em novos serviços de recomendação que ajudaram os utilizadores a definir melhor as configurações adequadas a um determinado dispositivo, minimizando o consumo energético e melhorando a sua eficiência. Adicionalmente toda esta informação pode ser utilizada para o diagnóstico de avarias e/ou manutenção preventiva. Em termos de proposta, o trabalho desenvolvido nesta tese tem as seguintes contribuições: Sistema de monitorização remota: o sistema de monitorização desenhado e implementado nesta tese avança o estado da arte dos sistemas de monitorização propostos pela literatura devido ao facto de incluir uma lista aprimorada de sensores que podem fornecer mais informações sobre os aparelhos, como o consumo de água da máquina de lavar. Além disso, é altamente flexível e pode ser implementado sem esforço em dispositivos novos ou antigos para monitorização de consumo de recursos. Conjunto de dados de consumo de energia de eletrodomésticos: Os dados recolhidos podem ser usados para futura investigação científica sobre o consumo de consumo de energia, padrões de uso de energia pelos eletrodomésticos e classificação dos mesmos. Abordagem de computação na borda (Edge Computing): O sistema de monitorização proposto explora o paradigma de computação na borda, onde parte da computação de preparação de dados é executada na borda, libertando recursos da nuvem para cálculos essenciais e que necessitem de mais poder computacional. Classificação precisa de dispositivos em tempo real: Coma proposta desenhada nesta tese, podemos classificar os dispositivos com alta precisão, usando os dados recolhidos pelo sistema de monitorização desenvolvido na tese. A abordagem proposta consegue classificar os dispositivos, que são monitorizados, com baixas taxas de falsos positivos. Para fácil compreensão do trabalho desenvolvido nesta tese, de seguida descreve-se a organização do documento. O Capítulo 1 apresenta o problema do consumo de energia na União Europeia e discute o aumento do consumo da mesma. O capítulo apresenta também os principais objetivos e contribuições do trabalho. No Capítulo 2 revê-se o trabalho relacionado em termos de sistema de monitorização remota, que inclui sensores, microcontroladores, processamento e filtragem de sinal. Por fim, este capítulo revê os trabalhos existentes na literatura relacionados com o problema de classificação de dispositivos usando abordagens de machine learning. No Capítulo 3 discutem-se os requisitos do sistema e o projeto de arquitetura conceitual do sistema. Neste capítulo é proposta uma solução de hardware, bem como, o software e firmware necessários à sua operação. Os algoritmos de machine learning necessários à classificação são também discutidos, em termos de configurações necessárias e adequadas ao problema que queremos resolver nesta tese. O Capítulo 4 representa a implementação de um protótipo que servirá de prova de conceito dos mecanismos discutidos no Capítulo 3. Neste capítulo discute-se também a forma de integração do protótipo na plataforma SATO. Com base na implementação feita, no Capítulo 5 especificam-se um conjunto de testes funcionais que permitem avaliar o desempenho da solução proposta e discutem-se os resultados obtidos a partir desses testes. Por fim, o Capítulo 6 apresenta as conclusões e o trabalho futuro que poderá ser desenvolvido partindo da solução atual.Energy consumption is daily growing in European Union (EU). One day it will become hardly sustainable. For this not to happen European Commission decided to implement a European Union Strategy, emphasizing two objectives: increasing energy efficiency and decarbonization. About 72% of all buildings in the EU are not adapted to be energy efficient. This problem encourages us to create solutions that would help assess the energy consumption of appliances at residential houses. In this thesis, we proposed a system that collects data using an intrusive load monitoring approach, where each appliance will have a dedicated monitoring rig to collect the energy consumption data. The proposed solution will help us understand the real-life consumption of each device being monitored and compare the laboratory measurements observed versus domestic consumption estimated by the energy consumption based on the EU energy efficiency labelling system. The system proposed detects device consumption patterns and recognize its brand, model and what actions that appliance is executing, e.g., program of washing in a washing machine. To achieve our goal, we designed a hardware solution capable of collecting sensor data, filtering and send it to a cloud platform (the SATO platform). Additionally, in the cloud, we have a Machine Learning solution that deals with the data and recognizes the appliance and its operation modes. This recognition allows drawing a device/settings profile, which can detect faults and create a recommendation service that helps users define the better settings for a specific appliance, minimizing energy consumption and improving efficiency. Finally, we examine our prototype approach of the system implemented for targeted objectives in this project report. The document describes the experiments that we did and the final results. Our results show that we can identify the appliance and some of its operation modes. The proposed approach must be improved to make the identification of all operation modes. However, the current version of the system shows exciting results. It can be used to support the design of a new labelling system where daily operation measures can be used to support the new classification system. This way, we have an approach that allows improving the energy consumption, making builds more efficient

Proceedings of the 6th International Conference EEDAL'11 Energy Efficiency in Domestic Appliances and Lighting

This book contains the papers presented at the sixth international conference on Energy Efficiency in Domestic Appliances and Lighting. EEDAL'11 was organised in Copenhagen, Denmark in May 2011. This major international conference, which was previously been staged in Florence 1997, Naples 2000, Turin 2003, London 2006, Berlin 200h9a s been very successful in attracting an international community of stakeholders dealing with residential appliances, equipment, metering liagnhdti ng (including manufacturers, retailers, consumers, governments, international organisations aangde ncies, academia and experts) to discuss the progress achieved in technologies, behavioural aspects and poliacineds , the strategies that need to be implemented to further progress this important work. Potential readers who may benefit from this book include researchers, engineers, policymakers, and all those who can influence the design, selection, application, and operation of electrical appliances and lighting.JRC.F.7-Renewable Energ

Smart Clothing Framework for Health Monitoring Applications

Wearable technologies are making a significant impact on people’s way of living thanks to the advancements in mobile communication, internet of things (IoT), big data and artificial intelligence. Conventional wearable technologies present many challenges for the continuous monitoring of human health conditions due to their lack of flexibility and bulkiness in size. Recent development in e-textiles and the smart integration of miniature electronic devices into textiles have led to the emergence of smart clothing systems for remote health monitoring. A novel comprehensive framework of smart clothing systems for health monitoring is proposed in this paper. This framework provides design specifications, suitable sensors and textile materials for smart clothing (e.g., leggings) development. In addition, the proposed framework identifies techniques for empowering the seamless integration of sensors into textiles and suggests a development strategy for health diagnosis and prognosis through data collection, data processing and decision making. The conceptual technical specification of smart clothing is also formulated and presented. The detailed development of this framework is presented in this paper with selected examples. The key challenges in popularizing smart clothing and opportunities of future development in diverse application areas such as healthcare, sports and athletics and fashion are discussed

Sustainability in design: now! Challenges and opportunities for design research, education and practice in the XXI century

Copyright @ 2010 Greenleaf PublicationsLeNS project funded by the Asia Link Programme, EuropeAid, European Commission