scholarly journals A Residential House Comparative Case Study Using Market Available Smart Plugs and EnAPlugs with Shared Knowledge

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1647 ◽  
Author(s):  
Luis Gomes ◽  
Filipe Sousa ◽  
Tiago Pinto ◽  
Zita Vale
Keyword(s):  
Energy Management ◽  
Management Systems ◽  
Comparative Case Study ◽  
Shared Knowledge ◽  
Monitoring And Control ◽  
Energy Management Systems ◽  
Multi Agent ◽  
Residential House ◽  
And Control

Smart home devices currently available on the market can be used for remote monitoring and control. Energy management systems can take advantage of this and deploy solutions that can be implemented in our homes. One of the big enablers is smart plugs that allow the control of electrical resources while providing a retrofitting solution, hence avoiding the need for replacing the electrical devices. However, current so-called smart plugs lack the ability to understand the environment they are in, or the electrical appliance/resource they are controlling. This paper applies environment awareness smart plugs (EnAPlugs) able to provide enough data for energy management systems or act on its own, via a multi-agent approach. A case study is presented, which shows the application of the proposed approach in a house where 17 EnAPlugs are deployed. Results show the ability to shared knowledge and perform individual resource optimizations. This paper evidences that by integrating artificial intelligence on devices, energy advantages can be observed and used in favor of users, providing comfort and savings.

scholarly journals An Intelligent Smart Plug with Shared Knowledge Capabilities

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3961 ◽  
Author(s):  
Luis Gomes ◽  
Filipe Sousa ◽  
Zita Vale
Keyword(s):  
Remote Monitoring ◽  
Low Cost ◽  
Resource Optimization ◽  
Management Systems ◽  
Smart Devices ◽  
Shared Knowledge ◽  
Monitoring And Control ◽  
Energy Management Systems ◽  
And Control

The massive dissemination of smart devices in current markets provides innovative technologies that can be used in energy management systems. Particularly, smart plugs enable efficient remote monitoring and control capabilities of electrical resources at a low cost. However, smart plugs, besides their enabling capabilities, are not able to acquire and communicate information regarding the resource’s context. This paper proposes the EnAPlug, a new environmental awareness smart plug with knowledge capabilities concerning the context of where and how users utilize a controllable resource. This paper will focus on the abilities to learn and to share knowledge between different EnAPlugs. The EnAPlug is tested in two different case studies where user habits and consumption profiles are learned. A case study for distributed resource optimization is also shown, where a central heater is optimized according to the shared knowledge of five EnAPlugs.

IoE-Based Control and Monitoring of Electrical Grids

2019 ◽  
pp. 57-82 ◽  
Author(s):  
Nelson Pinto ◽  
Dario Cruz ◽  
Jânio Monteiro ◽  
Cristiano Cabrita ◽  
Jorge Semião ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Monitoring And Control ◽  
Monitoring And Control System ◽  
Energy Management Systems ◽  
Management Measures ◽  
Electrical Grids ◽  
And Control ◽  
Levels Of Integration

In many countries, renewable energy production already represents an important percentage of the total energy that is generated in electrical grids. In order to reach higher levels of integration, demand side management measures are yet required. In fact, different from the legacy electrical grids, where at any given instant the generation levels are adjusted to meet the demand, when using renewable energy sources, the demand must be adapted in accordance with the generation levels, since these cannot be controlled. In order to alleviate users from the burden of individual control of each appliance, energy management systems (EMSs) have to be developed to both monitor the generation and consumption patterns and to control electrical appliances. In this context, the main contribution of this chapter is to present the implementation of such an IoT-based monitoring and control system for microgrids, capable of supporting the development of an EMS.

Sensor-based ambient intelligence for optimal energy efficiency

Sensor Review ◽  
2014 ◽  
Vol 34 (2) ◽  
pp. 170-181 ◽  
Author(s):  
David Robinson ◽  
David Adrian Sanders ◽  
Ebrahim Mazharsolook
Keyword(s):  
Energy Efficiency ◽  
Decision Support ◽  
Energy Consumption ◽  
Energy Management ◽  
Research Work ◽  
Management Systems ◽  
Manufacturing Companies ◽  
Content Type ◽  
Energy Management Systems

Purpose – This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation. Design/methodology/approach – A novel approach is taken to energy consumption monitoring by using ambient intelligence (AmI), extended data sets and knowledge management (KM) technologies. These are combined to create a decision support system as an innovative add-on to currently used energy management systems. Standard energy consumption data are complemented by information from AmI systems from both environment-ambient and process ambient sources and processed within a service-oriented-architecture-based platform. The new platform allows for building of different energy efficiency software services using measured and processed data. Four were selected for the system prototypes: condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase, and continuous improvement/optimisation of energy efficiency. Findings – An innovative and intelligent solution for energy efficiency optimisation is demonstrated in two typical manufacturing companies, within one case study. Energy efficiency is improved and the novel approach using AmI with KM technologies is shown to work well as an add-on to currently used energy management systems. Research limitations/implications – The decision support systems are only at the prototype stage. These systems improved on existing energy management systems. The system functionalities have only been trialled in two manufacturing companies (the one case study is described). Practical implications – A decision support system has been created as an innovative add-on to currently used energy management systems and energy efficiency software services are developed as the front end of the system. Energy efficiency is improved. Originality/value – For the first time, research work has moved into industry to optimise energy efficiency using AmI, extended data sets and KM technologies. An AmI monitoring system for energy consumption is presented that is intended for use in manufacturing companies to provide comprehensive information about energy use, and knowledge-based support for improvements in energy efficiency. The services interactively provide suggestions for appropriate actions for energy problem elimination and energy efficiency increase. The system functionalities were trialled in two typical manufacturing companies, within one case study described in the paper.

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