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
Ventura, Daniela; Casado-Mansilla, Diego; de Armentia, Juan López; Garaizar, Pablo; Lopez-de-Ipiña, Diego; Catania, Vincenzco
ARIIMA: A Real IoT Implementation of a Machine-Learning Architecture for Reducing Energy Consumption Conference
vol. 8867, Springer, Cham, 2014, ISBN: 978-3-319-13102-3.
Abstract | Links | BibTeX | Tags: ARIMA models, energy efficiency, IOT, machine learning
@conference{Ventura2014,
title = {ARIIMA: A Real IoT Implementation of a Machine-Learning Architecture for Reducing Energy Consumption},
author = {Daniela Ventura and Diego Casado-Mansilla and Juan López de Armentia and Pablo Garaizar and Diego Lopez-de-Ipiña and Vincenzco Catania},
url = {https://link.springer.com/chapter/10.1007/978-3-319-13102-3_72},
doi = {10.1007/978-3-319-13102-3_72},
isbn = {978-3-319-13102-3},
year = {2014},
date = {2014-12-01},
journal = {Lecture Notes in Computer Science },
volume = {8867},
publisher = {Springer, Cham},
abstract = {As the inclusion of more devices and appliances within the IoT ecosystem increases, methodologies for lowering their energy consumption impact are appearing. On this field, we contribute with the implementation of a RESTful infrastructure that gives support to Internet-connected appliances to reduce their energy waste in an intelligent fashion. Our work is focused on coffee machines located in common spaces where people usually do not care on saving energy, e.g. the workplace. The proposed approach lets these kind of appliances report their usage patterns and to process their data in the Cloud through ARIMA predictive models. The aim such prediction is that the appliances get back their next-week usage forecast in order to operate autonomously as efficient as possible. The underlying distributed architecture design and implementation rationale is discussed in this paper, together with the strategy followed to get an accurate prediction matching with the real data retrieved by four coffee machines.
},
keywords = {ARIMA models, energy efficiency, IOT, machine learning},
pubstate = {published},
tppubtype = {conference}
}
As the inclusion of more devices and appliances within the IoT ecosystem increases, methodologies for lowering their energy consumption impact are appearing. On this field, we contribute with the implementation of a RESTful infrastructure that gives support to Internet-connected appliances to reduce their energy waste in an intelligent fashion. Our work is focused on coffee machines located in common spaces where people usually do not care on saving energy, e.g. the workplace. The proposed approach lets these kind of appliances report their usage patterns and to process their data in the Cloud through ARIMA predictive models. The aim such prediction is that the appliances get back their next-week usage forecast in order to operate autonomously as efficient as possible. The underlying distributed architecture design and implementation rationale is discussed in this paper, together with the strategy followed to get an accurate prediction matching with the real data retrieved by four coffee machines.