Best household’s energy efficiency practices require awareness of energy prices, consumption, pollution and replacement options for inefficient appliances. This would allow household’s tenants to learn new habits, take measurements by their own and get knowledgeable about energy topics.
The proposed system enables tenant to receive recommendations and historical information to incentive good practices. Those recommendations are automatically generated by a combination of hardware and software components to provide valuable energy information automatically for tenant’s energy efficiency decision making. The technical side of the system relies on technologies such as Dart’s Flutter (Front-end), Python (Back-end), Amazon Web Services (Cloud service) or ngrok (port forwarding in private network).
To provide such information, the tenant has to plug in their appliances to a hardware device called Smart Plug which can gather energy consumption data from an appliance. After that, a front-end mobile application will guide through an on-boarding process in which the Smart Plug can be synced with a cloud service. Then, the tenant has to provide with the appliance’s label model information through the mobile’s camera and finally, the Smart Plug will be able to start gathering data to generate recommendations.
Best practices mean that the tenant must receive enough information to make decision of their appliances to replace or use it in different time-frames to reduce cost and pollution derived from energy consumption. Because of this, the mobile application provides with energy consumption data in kWh matching their corresponding prices, energy source mix and pollution for the days the appliance has been utilised in an user-friendly chart to enable easy visibility of trends, maximum and minimum values over time. This is separated into two user-interface sections: Consumption and Environmental. The first for energy consumption and price over time uses the REData database for energy in Spain and the other an accumulated values of energy sources pollution mix from worst to best. Finally, a recommendation section informs the tenant best time-frames to use his/her appliance (based on available data) to reduce cost. Additionially, if a better appliance is found in the European Product Database for Energy Labelling (EPREL) that theoretical reduces energy cost based on its tenant’s appliance consumption data, it will inform the cost benefit and let the tenant see more information about it.
Requirements have been defined by means of user stories in the form of “As…I want…So that…” and derived tasks in the form of “Given…when…Then…” for acceptance criteria. Once requirements are presented, the design of the project is explained with a high-level overview of the architecture decomposed in their main components: Front-end (Mobile application), Back-end (Cloud server), Devices (Energy monitoring device) and Integrator-Link (Local server at home), followed by the way they interact, highlighting the importance of the back-end’s role as integrating the mobile application with the Integrator Link.
In order to validate the final system, it was tested in three main use cases: Onboarding an appliance, daily syncing process, recommendation and pollution information.
The design of the software ensures that it is not only able to generate the required information, but it stands out in its integration abilities with energy and product sources, as well as its flexibility to support multiple brand of devices, locations, label identification and appliances searching characteristics for a better user experience. All these features lead to a system capable, in future works, of integrating a larger variety of devices such as Smart Appliances, increasing the use of renewable energy sources for houses and improving the visualization through immersive user-interfaces.
Best household’s energy efficiency practices require awareness of energy prices, consumption, pollution and replacement options for inefficient appliances. This would allow household’s tenants to learn new habits, take measurements by their own and get knowledgeable about energy topics.
The proposed system enables tenant to receive recommendations and historical information to incentive good practices. Those recommendations are automatically generated by a combination of hardware and software components to provide valuable energy information automatically for tenant’s energy efficiency decision making. The technical side of the system relies on technologies such as Dart’s Flutter (Front-end), Python (Back-end), Amazon Web Services (Cloud service) or ngrok (port forwarding in private network).
To provide such information, the tenant has to plug in their appliances to a hardware device called Smart Plug which can gather energy consumption data from an appliance. After that, a front-end mobile application will guide through an on-boarding process in which the Smart Plug can be synced with a cloud service. Then, the tenant has to provide with the appliance’s label model information through the mobile’s camera and finally, the Smart Plug will be able to start gathering data to generate recommendations.
Best practices mean that the tenant must receive enough information to make decision of their appliances to replace or use it in different time-frames to reduce cost and pollution derived from energy consumption. Because of this, the mobile application provides with energy consumption data in kWh matching their corresponding prices, energy source mix and pollution for the days the appliance has been utilised in an user-friendly chart to enable easy visibility of trends, maximum and minimum values over time. This is separated into two user-interface sections: Consumption and Environmental. The first for energy consumption and price over time uses the REData database for energy in Spain and the other an accumulated values of energy sources pollution mix from worst to best. Finally, a recommendation section informs the tenant best time-frames to use his/her appliance (based on available data) to reduce cost. Additionially, if a better appliance is found in the European Product Database for Energy Labelling (EPREL) that theoretical reduces energy cost based on its tenant’s appliance consumption data, it will inform the cost benefit and let the tenant see more information about it.
Requirements have been defined by means of user stories in the form of “As…I want…So that…” and derived tasks in the form of “Given…when…Then…” for acceptance criteria. Once requirements are presented, the design of the project is explained with a high-level overview of the architecture decomposed in their main components: Front-end (Mobile application), Back-end (Cloud server), Devices (Energy monitoring device) and Integrator-Link (Local server at home), followed by the way they interact, highlighting the importance of the back-end’s role as integrating the mobile application with the Integrator Link.
In order to validate the final system, it was tested in three main use cases: Onboarding an appliance, daily syncing process, recommendation and pollution information.
The design of the software ensures that it is not only able to generate the required information, but it stands out in its integration abilities with energy and product sources, as well as its flexibility to support multiple brand of devices, locations, label identification and appliances searching characteristics for a better user experience. All these features lead to a system capable, in future works, of integrating a larger variety of devices such as Smart Appliances, increasing the use of renewable energy sources for houses and improving the visualization through immersive user-interfaces. Read More
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