2016
Casado-Mansilla, Diego; de Armentia, Juan López; Ventura, Daniela; Garaizar, Pablo; Lopez-de-Ipiña, Diego
Embedding Intelligent Eco-aware Systems within Everyday Things to Increase People’s Energy Awareness Journal Article
In: Soft Computing, vol. 20, no. 5, pp. 1695-1711, 2016.
Abstract | Links | BibTeX | Tags: ARIMA models, Eco-aware everyday things, Energy awareness, machine learning, Persuasive eco-feedback, Time series
@article{Casado-Mansilla2016,
title = {Embedding Intelligent Eco-aware Systems within Everyday Things to Increase People’s Energy Awareness},
author = {Diego Casado-Mansilla and Juan López de Armentia and Daniela Ventura and Pablo Garaizar and Diego Lopez-de-Ipiña },
url = {https://morelab.deusto.es/media/publications/2015/journalarticle/embedding-intelligent-eco-aware-systems-within-everyday-things-to-increase-peoples-energy-awareness.pdf},
doi = {10.1007/s00500-015-1751-0},
year = {2016},
date = {2016-05-01},
journal = {Soft Computing},
volume = {20},
number = {5},
pages = {1695-1711},
abstract = {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 are transformed into time-series and then processed to obtain the appliances’ next-week usage forecast. Autoregressive integrated moving average 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. 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 device 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.
},
keywords = {ARIMA models, Eco-aware everyday things, Energy awareness, machine learning, Persuasive eco-feedback, Time series},
pubstate = {published},
tppubtype = {article}
}
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 are transformed into time-series and then processed to obtain the appliances’ next-week usage forecast. Autoregressive integrated moving average 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. 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 device 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.
2015
Casado-Mansilla, Pablo; de Armentia, Juan López; Ventura, Daniela; Garaizar, Pablo; Lopez-de-Ipiña, Diego
Embedding Intelligent Eco-aware Systems within Everyday Things to Increase People’s Energy Awareness Journal Article
In: Soft Computing Journal, vol. 20, no. 5, pp. 1695-1711, 2015, ISSN: 1433-7479.
Abstract | Links | BibTeX | Tags: ARIMA models, Eco-aware everyday things, Energy awareness, machine learning, Persuasive eco-feedback, Time series
@article{Casado-Mansilla2015,
title = {Embedding Intelligent Eco-aware Systems within Everyday Things to Increase People’s Energy Awareness},
author = {Pablo Casado-Mansilla and Juan López de Armentia and Daniela Ventura and Pablo Garaizar and Diego Lopez-de-Ipiña},
url = {https://link.springer.com/content/pdf/10.1007%2Fs00500-015-1751-0.pdf},
doi = {10.1007/s00500-015-1751-0},
issn = {1433-7479},
year = {2015},
date = {2015-06-26},
journal = {Soft Computing Journal},
volume = {20},
number = {5},
pages = {1695-1711},
abstract = {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 are transformed into time-series and then processed to obtain the appliances’ next-week usage forecast. Autoregressive integrated moving average 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. 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 device 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.},
keywords = {ARIMA models, Eco-aware everyday things, Energy awareness, machine learning, Persuasive eco-feedback, Time series},
pubstate = {published},
tppubtype = {article}
}
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 are transformed into time-series and then processed to obtain the appliances’ next-week usage forecast. Autoregressive integrated moving average 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. 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 device 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.
2014
Casado-Mansilla, Diego; de Armentia, Juan López; Garaizar, Pablo; Lopez-de-Ipiña, Diego
Team Up with Eco-Aware Everyday Things to Green Your Workplace!. Conference
Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), IEEE, 2014, ISBN: 978-1-4799-4331-9.
Abstract | Links | BibTeX | Tags: Eco-aware everyday things, eco-feedback, Persuasive Technology, teammates
@conference{Casado-Mansilla2014b,
title = {Team Up with Eco-Aware Everyday Things to Green Your Workplace!.},
author = {Diego Casado-Mansilla and Juan López de Armentia and Pablo Garaizar and Diego Lopez-de-Ipiña },
url = {https://ieeexplore.ieee.org/document/6975498/},
doi = {10.1109/IMIS.2014.55},
isbn = { 978-1-4799-4331-9},
year = {2014},
date = {2014-07-04},
booktitle = {Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS)},
publisher = {IEEE},
abstract = {The lack of energy consumption awareness in public spaces is a fact. There, people do not receive energy consumption feedback nor do they pay a monthly invoice to electricity providers. Thus, there is practically a non-existent perception of energy waste, and hence, there is low motivation to reduce it. To tackle this problem we transform everyday shared electrical appliances which are placed in common spaces into collaborative eco-aware everyday things. These eco-appliances make people aware that they are not alone to save energy, but the everyday things can team up with them to achieve this task. Qualitative and quantitative results were gathered in three case studies performed with shared coffee machines at workplace. The objective was to assess the effectiveness of the proposed eco-aware design in terms of energy saving and the degree of affiliation between workers and the smart appliance to create a green-team relationship.
},
keywords = {Eco-aware everyday things, eco-feedback, Persuasive Technology, teammates},
pubstate = {published},
tppubtype = {conference}
}
The lack of energy consumption awareness in public spaces is a fact. There, people do not receive energy consumption feedback nor do they pay a monthly invoice to electricity providers. Thus, there is practically a non-existent perception of energy waste, and hence, there is low motivation to reduce it. To tackle this problem we transform everyday shared electrical appliances which are placed in common spaces into collaborative eco-aware everyday things. These eco-appliances make people aware that they are not alone to save energy, but the everyday things can team up with them to achieve this task. Qualitative and quantitative results were gathered in three case studies performed with shared coffee machines at workplace. The objective was to assess the effectiveness of the proposed eco-aware design in terms of energy saving and the degree of affiliation between workers and the smart appliance to create a green-team relationship.
Casado-Mansilla, Diego; de Armentia, Juan López; Garaizar, Pablo; Lopez-de-Ipiña, Diego
To Switch off the Coffee-maker or Not: That is the Question to be Energy-efficient at Work Conference
2014, ISBN: 978-1-4503-2474-8.
Abstract | Links | BibTeX | Tags: Eco-aware everyday things, eco-feedback, Energy-Efficiency, Persuasive Technology, Sustainability
@conference{Casado-Mansilla2014,
title = {To Switch off the Coffee-maker or Not: That is the Question to be Energy-efficient at Work},
author = {Diego Casado-Mansilla and Juan López de Armentia and Pablo Garaizar and Diego Lopez-de-Ipiña},
doi = {10.1145/2559206.2581152},
isbn = {978-1-4503-2474-8},
year = {2014},
date = {2014-04-01},
pages = {2425-2430},
abstract = {There are some barriers to reduce energy consumption in shared spaces where many people use common electronic devices (e.g. dilution of responsibility, the trade-off between comfort and necessity, absentmindedness, or the lack of support to foster energy-efficiency). The workplace is a challenging scenario since the economic incentives are not present to increase energy awareness. To tackle some of these issues we have augmented a shared coffee-maker with eco-feedback to turn it into a green ally of the workers. Its design rationale is twofold: Firstly, to make the coffee-maker able to learn its own usage pattern. Secondly, to communicate persuasively and in real-time to users whether it is more efficient to leave the appliance on or off during certain periods of time along the workday. The goal is to explore a human-machine team towards energy efficiency and awareness, i.e. whether giving the initiative to users to decide how to operate the common appliances, but being assisted by them, is a better choice than automation or mere informative eco-feedback.},
keywords = {Eco-aware everyday things, eco-feedback, Energy-Efficiency, Persuasive Technology, Sustainability},
pubstate = {published},
tppubtype = {conference}
}
There are some barriers to reduce energy consumption in shared spaces where many people use common electronic devices (e.g. dilution of responsibility, the trade-off between comfort and necessity, absentmindedness, or the lack of support to foster energy-efficiency). The workplace is a challenging scenario since the economic incentives are not present to increase energy awareness. To tackle some of these issues we have augmented a shared coffee-maker with eco-feedback to turn it into a green ally of the workers. Its design rationale is twofold: Firstly, to make the coffee-maker able to learn its own usage pattern. Secondly, to communicate persuasively and in real-time to users whether it is more efficient to leave the appliance on or off during certain periods of time along the workday. The goal is to explore a human-machine team towards energy efficiency and awareness, i.e. whether giving the initiative to users to decide how to operate the common appliances, but being assisted by them, is a better choice than automation or mere informative eco-feedback.