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Strategies to Enhance Energy Efficiency of Coffee Machines
Abstract
Coffee machines use large amounts of electricity for permanent ready (keeping hot) and standby modes. With relatively simple measures as auto-power-down, better insulation of boilers and low standby the energy efficiency can be strongly enhanced. The energy saving potential of an efficient versus a typical espresso machine is about 120 kWh per year. High efficiency coffee machines only have a consumption of about 50 kWh per year, capsule machines even below 40 kWh. The entire EU stock of coffee machines (estimated 100 Mio) thus holds a saving potential of up to 12"000 Mio kWh per year. Conventional espresso machines usually have higher electricity consumption than A-class ovens or A++ refrigerators. Regarding the great differences between products and the high saving potentials, it is strongly recommended to take measures. In the framework of the IEE-project "Euro-Topten" a measuring method for coffee machines was developed. The Blue Angel (der Blaue Engel) now follows a new cluster approach. As part of this approach it has launched a climate protection label (Klimaschutzzeichen). Thereby Euro-Topten and the Blue Angel coordinated their procedure and harmonized the measurement method. The criteria are applied for the label the Blue Angel and for the selection of best products of Europe on www.topten.info. The Euro-Topten measuring method is suggested for a labeling directive and might be incorporated into the IEC 60661 standard (Methods for measuring the performance of electric household coffee makers), respectively.
... For conformity purpose, legislations and labels were based on definite tests for qualifying power (European Commission 2008b;US EPA 2009). In order to compare products efficiencies, (Bush et al. 2009;Meier 1995) (Bush et al. 2009)). Many life cycle assessments of products (for example (Kim et al. 2001;Moberg et al. 2010)) use industrial data based on direct measurements on products. ...
... For conformity purpose, legislations and labels were based on definite tests for qualifying power (European Commission 2008b;US EPA 2009). In order to compare products efficiencies, (Bush et al. 2009;Meier 1995) (Bush et al. 2009)). Many life cycle assessments of products (for example (Kim et al. 2001;Moberg et al. 2010)) use industrial data based on direct measurements on products. ...
... (Kim et al. 2001) defined three modes for computer monitors: 4 hours per day in the on mode, 4h per day in the energy saving mode (similar to the stand-by mode), the rest of the time in the off mode, each of them with their own associated power consumption (P on ; P standby ; P off ). (Moberg et al. 2010) used three modes for the evaluation of an e-paper reader over 1 year of use: 30 minutes per day reading newspapers (on mode), 30 minutes per day for other usages (setting, downloading papers, reading e-books…-stand-by mode) and the rest of the time in the off mode. Similarly, in order to define the "best-in-class" product for espresso coffee machine, the "top ten" initiative (Bush et al. 2009) defined the maximum power for three different time periods: the ready mode (ie. on mode), the sleep mode (ie. stand by) and the auto-power off mode (product switch itself off after a certain time without making coffee). ...
One of the challenging environmental issues faced by the electr(on)ic industry is the development of energy-efficient products during their use. Indeed, regulations, standards, and consumers always have growing expectations on this aspect. Nevertheless, tools to support design for energy efficiency in use are scarce and do not always give an appropriate answer to the challenge. This paper presents a new methodology based on the calculation of an indicator that enables a design team to drive energy efficiency more effectively during the design process. The indicator combines the power of components with time spent by these components to do their jobs, in order to lead to energy consumption estimation. When used for design purposes, the method can provide the team with a relevant analysis of the energetic performances of the product, including modes, jobs and scenarios variations. This analysis can lead to products’ hot spots and eventually to design strategies. A case study on an existing electr(on)ic equipment is proposed for illustration purposes.
... An example of this approach is environment automation, where smart machines are energy-efficient and make the sustainability-oriented decisions on their own (e.g. Starik and Marcus (2000); Bush et al. (2009)). 2. human behavioural change approach where people bear the full responsibility of their decisions (e.g. ...
... Although some academics could estimate that it is simplistic to just focus the efficiency approach to one appliance, we argue that whatever minimal energy reduction achieved with these devices will have a significant impact in terms of energy. The stock of electric coffee machines in EU in 2009 was estimated to be 100 million units (whereas a estimation of coffee machines in work-offices attained 30 million), consuming 17000 million of kWh per year (Bush et al. (2009)). Moreover, this tendency is prone to continue increasing and we have witnessed during our research that there is not a huge difference in terms of energy efficiency between old-fashioned models and new ones. ...
There is a lack of energy consumption awareness in working spaces. People in their workplaces do not receive energy consumption feedback nor do they pay a monthly invoice to electricity providers. In order to enhance workers' energy awareness, we have transformed everyday shared electrical appliances which are placed in common spaces (e.g. beamer projectors, coffee-makers, printers, screens, portable fans, kettles, and so on.) into persuasive eco-aware everyday things. The proposed approach lets these appliances report their usage patterns to a Cloud-server where the data is transformed into time-series and then processed to obtain the appliances' next-week usage forecast. Autoregressive Integrated Moving Average (ARIMA) model has been selected as the potentially most accurate method for processing such usage predictions when compared with the performance exhibited by three different configurations of Artificial Neural Networks (ANNs). Our major contribution is the application of soft computing techniques to the field of sustainable persuasive technologies. Thus, consumption predictions are used to trigger timely persuasive interactions to help devices users to operate the appliances as efficiently, energy-wise, as possible. Qualitative and quantitative results were gathered in a between-three-groups study related with the use of shared electrical coffee-makers at workplace. The goal of these studies was to assess the effectiveness of the proposed eco-aware design in a workplace environment in terms of energy saving and the degree of affiliation between people and the smart appliances to create a green-team relationship.
... The same authors argue that by applying simple measures as auto-power-down, better insulation of boilers and low standby consumption, the energy efficiency can be strongly enhanced. Although in a similar report from SAFE group [21] it is stated that the capsules coffee machine's consumption is relatively low, 40 kWh per year, we think it is possible to reduce this consumption further by applying more intelligence over these devices, beyond static eco-design, according to their usage pattern. ...
... This coffee machine, in particular, is designed to operate in two different modes: The On-Off mode (a combination of previously surveyed Active and No-power modes) and the Standby mode, also referred to as 'keeping hot' [22]. However, boosted by the EU recommendation [21], this model of coffee machine is also programmed with an on-the-self energy efficient mode, which is called Auto-power down mode. Figure 3 shows three state diagrams of the modes of operation that the off-the-shelf capsule-based coffee machine performs. Before explaining them, let's first recall that this coffee-maker model has a start button in order to switch it on and switch it off, and a LED light that shifts from blinking to steady to warn users when the device is ready to be used. ...
Society wastes much more energy than it should. This produces tons of unnecessary CO_2 emissions. This is partly due to the inadequate use of electrical devices given the intangible and invisible nature of energy. This misuse of devices and energy unawareness is particularly relevant in public spaces (offices, schools, hospitals and so on), where people use electrical appliances, but they do not directly pay the invoice to energy providers. Embedding intelligence within public, shared appliances, transforming them into Eco-aware things, is valuable to reduce a proportion of the unnecessarily consumed energy. To this end, we present a twofold approach for better energy efficiency in public spaces: 1) informing persuasively to concerned users about the misuse of electronic appliances; 2) Customizing the operating mode of this everyday electrical appliances as a function of their real usage pattern. To back this approach, a capsule-based coffee machine placed in a research laboratory has been augmented. This device is able to continuously collect its usage pattern to offer feedback to coffee consumers about the energy wasting and also, to intelligently adapt its operation to reduce wasted energy. To this aim, several machine learning approaches are compared and evaluated to forecast the next-day device usage.
... These brewers produce coffee by allowing heated water to slowly drip through a filter containing coffee grounds, while coffee is stored in a con-tinuously heated reservoir. Most of these machines do not include auto-power down features and thus consume up to three fourth of their total electricity consumption in the standby mode to keep the beverage hot ( Bush et al., 2009 ). On the contrary, single-serve coffee makers force just the exact volume of hot water through prepackaged coffee dispensers, such as pods and capsules. ...
Roasted and ground coffee in Italy is currently dominating in consumption and revenues. However, the portioned coffee market is recording continuous and constant growth notwithstanding a great concern about the unnecessary consumption of non-renewable resources and huge generation of often aluminum-polluted wastes.
The aim of this work was to assess the cradle-to-grave carbon footprint of a 40-mL cup of coffee, prepared using different brewing methods (i.e., a 3-cup Moka pot, and three single-serving coffee machines) and distinct formats (i.e., 250-g vacuum flexible bags, 44-mm Easy Serving Espresso pods, and Nespresso®-type capsules), in compliance with the Publicly Available Specification (PAS) 2050 standard method.
The production of one cup of coffee gave rise to 45-57 or 47-59 g CO2e when using the Moka pot heated by an induction- or gas-fired stove, 74-96 g CO2e when using the espresso coffee machine, 72-92 g CO2e when using the pod coffee machine, and 57-73 g CO2e when using the capsule coffee machine, whether the post-consumer wastes were incinerated or disposed of in landfills. Moreover, by using coffee pods or capsules, the contribution of the packaging material production and post-consumer waste disposal increased up to represent the secondary share of the overall GHG emissions. Based on these estimates, the environmentally aware consumer should be conscious that the preparation of a cup of coffee with a coffee pod or capsule machine would result in extra emissions of 27.6 or 12.6 g CO2e with respect to those emitted with an induction Moka pot, respectively. By referring to the current Italian consumption of 80-million coffee cups per day, such extra GHG emissions would equal to those emitted by a Euro 5 city car (ca. 100 g CO2e/km) traveling around the Earth's equator as many as 551 or 252 times per day, respectively.
... Characteristics of MELs along with methods for reducing their consumption have subsequently been examined to the greatest extent possible in applications such as new homes (Brown 2007), hospitals (Black 2014 andChristiansen 2015), consumer electronics (Roth 2014 and Consumer Technology Association 2017), coffee makers (Bush 2009 andEnergy Star 2011), and electric motors in both residential and commercial applications (Goetzler 2013). However, these characteristics are largely dependent on the type of end-use load or device. ...
Miscellaneous energy loads (MELs) comprise a significant and growing portion of total building energy consumption. However relatively little is known about the products that make up MELs end uses, and MELs are modeled with less granularity than major end uses in energy demand models such as the U.S. Department of Energy's Scout energy efficiency impact analysis tool or the U.S. Energy Information Administration's National Energy Modeling System (NEMS). This paper identifies differences in the way MELs versus major building end uses are modeled, and then reviews potential sources of MELs baseline technology data that could be used to improve their characterization in energy demand models. Case studies of dry distribution transformers, hot water circulation pumps, and Japanese-style automatic bidets are presented to compare and contrast the level of baseline data available for different end uses. We show that there is potential to use data from energy conservation standards and other sources to significantly improve representation of some MELs in energy demand models, while other emerging MELs require further data collection before they can be modeled precisely.
... A coffee machine redesign was used for comparing the different concepts that could be implemented depending on the segment it was designed for. This product was chosen because most of its environmental impact happens during use phase and this impact is influenced by user behavior (Bush et al., 2009). Ecodesign strategies were compiled from Mac Donald and She (2013) and Tang and Bhamra (2008) to support concept development. ...
Market segmentation has been a crucial point for enhancing the success of new products. By grouping users with similar needs, manufacturers have been able to design products with much more appeal to a specific typology of users. Moreover, ecodesign process strives at developing products with lower impact on the environment. Actual ecodesigned products however do not completely fit with users needs. This contribution proposes to use the segmentation approach to drive the process of product ecodesign. By differencing the users according to their sensitivity to environmental issues, this approach enables to design environmentally friendly products for a specific group. To this end, we intend to couple an existing segmentation definition for environmental policy with the Rokeach Values System. The result is a seven group representation of users with a specific set of values for each group that will allow developing products that match user needs with the environmental consciousness. This segmentation and its benefits are applied to the adaptation of ecodesign strategies according to each users group. We illustrate our proposal with an application to the design of a coffee machine.
Over time, miscellaneous electric loads (MELs) are expected to increase both in magnitude and share of residential and commercial building energy consumption. This trend is most apparent in North America, but it is also occurring in Japan and Europe. However, the contribution of MELs to building energy use is not currently well understood, both because the products in this category are transforming rapidly and the definition and classification of MELs is ambiguous. This study estimated the national energy consumption of 36 MELs using best-available data and found them to comprise 12% of delivered electricity to the U.S. residential and commercial building sectors. If 26 of these MELs were replaced with the most energy-efficient product models available on the market, their energy consumption could be halved to 6% of delivered electricity. National energy models will better account for building energy consumption by incorporating the MELs data collected and analyzed for this study, leading to improved policy decisions.
The stock of coffee machines in the European Union is estimated to be 100 million units, consuming 17'000 million kWh per year and causing electricity costs of about 2'500 Mio Euro (according to estimations by Topten). Roughly 20 million coffee machines are annually sold in Europe. For comfort and quality reasons, the trend goes towards espresso machines (fully automatic machines, portioned machines with high pressure) and filter pad machines; by now those machines account for about 45% of total sales, while the rest are mainly traditional drip-filter machines. On account of the high energy saving potentials, manufacturers have implemented various efforts to increase the energy efficiency of coffee machines in the past few years. Energy efficiency particularly was enhanced with measures such as auto-power-down, better insulation of hot parts and low or zero standby consumption. One striking development towards super efficient coffee machines is the application of flow-type heaters. Owing to the introduction of this technology by some innovative manufacturers these types of coffee machines represent the current best available technology (BAT). This paper discusses the energy saving technology of flow-type heater for coffee machines, it gives an overview of the current state of the IEC 60661 standard (update in progress) and the EU Eco-design process (Lot 25) and it presents policy measures on European Minimum Energy Performance Standards MEPS and a labelling directive.
The environmental impacts of coffee consumption inter alia depend on the preparation method used by consumers. Preparation methods such as filter drip, pod machines and fully automatic coffee machines are the most common ones in Germany: 62% of the consumers use a filter drip machine to brew their coffee, 23% use filter pad machines and 15% use espresso machines such as fully automatic coffee machines or capsule systems. The aim of the different studies presented in this paper was to identify the critical environmental issues along the life cycle of coffee and to compare the different preparation methods of coffee regarding their influence on the environmental impacts. Within the Product Carbon Footprint (PCF) Pilot Project Germany, the PCF of one cup of a special type of coffee was analysed on behalf of Tchibo GmbH (Überseering, Hamburg, Germany). As the results show, the preparation by the consumer is one crucial part of the entire life cycle of coffee, making up a share of 30% of the overall emissions. Another hot spot is the cultivation of coffee beans with 55%. Concerning the use phase, research shows that environmental impacts vary significantly depending on the preparation method used by the consumer. Main drivers are differences in power consumption of the respective technologies. Furthermore, different packagings of the coffee play a decisive role. Comparing the analysed appliances and defined usage scenarios in this study, the French press and filter drip machine performed best, followed by the filter pad machine. In contrast, the environmental impacts of the analysed fully automatic coffee machine and the capsule machine were highest. The reason for this was the high power consumption, especially in the machines' sleep and standby mode. Additionally, capsule machines contribute to the environmental impacts because of the aluminium and/or plastic packaging of the capsules, automatic coffee machines because of their cleaning and rinsing programmes.
Stand-by consumption of household appliances
- Swiss Agency
- Efficient
- S A F E Use
- J Nipkow
- E Bush
Swiss Agency for Efficient Energy Use S.A.F.E., Nipkow, J., Bush, E.: Stand-by consumption of household appliances, Presentation at EEDAL 2003 Conference, Torino.