The EnerGAware middleware platform

Residential buildings can contribute to save energy and to decrement electricity consumption in the world. On the other hand, the Internet of Things has allowed the implementation of smart homes that can pro le the users. Nevertheless, end-users are not accepting the smart homes due to behavioral problems and usability problems with the Human-Machine Interface (HMI) or with the household appliances. As a solution, social products promote the interaction between the smart home and the consumer by including gami cation features in the interface. Thus, smart homes can interact and compete with other houses to reduce energy consumption. Therefore, this paper proposes a three-step framework that takes advantage of social products to promote interaction between smart homes within a smart community to reduce electrical energy consumption. The rst step collects from the literature review, the characteristics of the end-users, the behavioral and usability problems, and the most common gami cation elements that teach, engage and motivate the user to reduce energy consumption. The second step proposes the gami cation elements required for a tailored HMI in each social product and smart home through a fuzzy logic decision. The third step evaluates the interaction between social products in smart homes and the users to test which smart home is reducing more energy consumption. Finally, a three-level tailored gami ed mock-up is depicted: level 1 for a single social product, level 2 for the smart home, and level three for the smart community. This mock-up can be implemented in small communities as residential complexes.

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“…Energy Battle [82], Residence Energy Saving Battle [83], Energy Chickens [84], EnergyCat [85,86], Virtual city [87], Energy 2020 [88,89].…”

Section: Gami Cation In Energy Applicationsmentioning

confidence: 99%

Empower saving energy into smart communities using social products with a gamification structure for tailored Human–Machine Interfaces within smart homes

Méndez

Ponce

Meier

et al. 2022

Int J Interact Des Manuf

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“…c) Achievement reward: Users are rewarded with a cue for unlocking the achievement. Usually, these achievements are visible to players (AlSkaif et al, 2018;Barbosa et al, 2017;Bartle, 1996;Casals et al, 2017;Chou, 2015;Dorji et al, 2015;ecoGator, 2016;Fijnheer and Van Oostendorp, 2016;Game | 2020Energy, 2018Geelen et al, 2012;Hamari and Eranti, n.d.;Matallaoui et al, 2015;Ouariachi et al, 2019;Stieglitz et al, 2016). Game-related: Players earn points toward the maximum achievement points possible as a manner to dispose and accumulate high-value rewards (Bartle, 1996).…”

Section: Knowledge Base Phasementioning

confidence: 99%

“…motivation. An attractive user interface is required with stimulation visuals and exciting interaction concepts (AlSkaif et al, 2018; Barbosa et al, 2017;Casals et al, 2017;Chou, 2015;Dorji et al, 2015;ecoGator, 2016;Fijnheer and Van Oostendorp, 2016;Game | 2020Energy, 2018Geelen et al, 2012;Hamari and Eranti, n.d.;Matallaoui et al, 2015;Moloney et al, 2017;Ouariachi et al, 2019;Stieglitz et al, 2016). SG: This option is available from step 1 to step 5, the Install and ready, Build to Suit, Canvas Master, the Energy Master, and the Win to yourself achievements.…”

Section: Knowledge Base Phasementioning

confidence: 99%

Tailored gamification and serious game framework based on fuzzy logic for saving energy in connected thermostats

Ponce

Meier

Méndez

et al. 2020

Journal of Cleaner Production

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Connected thermostats (CTs) often save less energy than predicted because consumers may not know how to use them and may not be engaged in saving energy. Additionally, several models perform contrary to consumers' expectations and are thus not used the way they are intended to. As a result, CTs save less energy and are underused in households. This paper reviews aspects of gamification and serious games focused on engaging consumers. A gamification and serious games framework is proposed for saving energy that is tailored by a fuzzy logic system to motivate connected thermostat consumers. This intelligent gamification framework can be used to customize the gamification and serious game strategy to each consumer so that fuzzy logic systems can be adapted according to the requirements of each consumer. The framework is designed to teach, engage, and motivate consumers while helping them save electrical energy when using their thermostats. It is described the proposed framework as well as a mockup that can be run on a cellphone. Although this framework is designed to be implemented in CTs, it can be translated to their energy devices in smart homes.

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“…This is done through (a) collecting and aggregating data from various sources; (b) engaging consumers in the adventure of playing a serious game that is connected to real-time data collection; and (c) providing actionable feedback to reduce wasted energy. In [224], the EnerGAware platform essentially focuses on providing a serious game heavily backed by IoT sensors fusion and integration using a service management system based on publish/subscribe approach. The main purpose of the serious game is to improve the likelihood of behavioral change for energy consumers via education and training.…”

Section: Comparison Of Energy Efficiency Projectsmentioning

confidence: 99%

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Himeur,

Alsalemi,

Al-Kababji

et al. 2020

Preprint

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Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-theart data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored.Keywords Data fusion • energy efficiency • sensors • appliance identification • fusion of 2D descriptors • machine learning.

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The EnerGAware middleware platform

Cited by 6 publications

References 5 publications

Empower saving energy into smart communities using social products with a gamification structure for tailored Human–Machine Interfaces within smart homes

Empower saving energy into smart communities using social products with a gamification structure for tailored Human–Machine Interfaces within smart homes

Tailored gamification and serious game framework based on fuzzy logic for saving energy in connected thermostats

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

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