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Augmented reality (AR) improves how we acquire, understand, and display information without distracting us from the real world. These technologies can be used in different applications and industries as they can incorporate domain-specific visualizations on a real-world screen. Mobile augmented reality (MAR) essentially consists of superimposing virtual elements over real objects on the screen, to give added value and enrich the interaction with reality. In numerous plants, it is being used for maintenance and repair tasks, as well as training. The Internet of Things (IoT) is increasingly pervading every aspect of our lives, including the power infrastructure of our buildings. IoT-enabled devices offer many connectivity options for helping supervise all-important energy assets. Aggregating data to cloud-based platforms enables operations teams to have on-time information access to make fast decisions and have a fast response regarding energy use, while maintenance teams keep on top of the appliance power quality and reliability needed by using MAR. This paper presents a novel approximation for visualizing appliance-related power quality to enhance awareness about the consumed electricity. A combined solution of MAR with IoT technologies is employed. Engineered solutions’ hands-free way to get data about surrounding appliances reduces the complexity, saves energy, and speeds up the operations. An innovative way to measure things at the right time leads to a competitive advantage.

The effort to continuously improve and innovate smart appliances (SA) energy management requires an experimental research and development environment which integrates widely differing tools and resources seamlessly. To this end, this paper proposes a novel Direct Load Control (DLC) testbed, aiming to conveniently support the research community, as well as analyzing and comparing their designs in a laboratory environment. Based on the LabVIEW computing platform, this original testbed enables access to knowledge of major components such as online weather forecasting information, distributed energy resources (e.g., energy storage, solar photovoltaic), dynamic electricity tariff from utilities and demand response (DR) providers together with different mathematical optimization features given by General Algebraic Modelling System (GAMS). This intercommunication is possible thanks to the different applications programming interfaces (API) incorporated into the system and to intermediate agents specially developed for this case. Different basic case studies have been presented to envision the possibilities of this system in the future and more complex scenarios, to actively support the DLC strategies. These measures will offer enough flexibility to minimize the impact on user comfort combined with support for multiple DR programs. Thus, given the successful results, this platform can lead to a solution towards more efficient use of energy in the residential environment.Energies 2019, 12, 3336 2 of 16 since 2010 [3]. Although the electricity demand for major appliances has slightly decreased since 2007, mainly due to improvements in their energy efficiency, the rapid proliferation of small appliances and brown goods has absorbed these savings. The energy consumption due to these small loads has grown twice as fast as that of large appliances in the last decade. In addition, only one-third of domestic appliances consumption is under regulatory protection, particularly in emerging markets. This may become even more relevant in the near future as the demand for electricity in buildings increases due to the impact of the charging infrastructure for electric vehicles. While it is true that there is a need to increase the rigor of existing policies by extending regulatory coverage to a broader range of devices, on the other hand, user awareness may be the key factor. However, to achieve this, consumers should be rewarded to some extent when changing their behavior. The availability of information and communication technologies (ICT) on SG can be decisive in meeting this commitment through the widespread adoption of DR strategies.In other areas, such as power electronics, it is common to find a complete chain of modeling, development, testing, optimization, virtual validation, and rapid prototyping commercial tools that integrate seamlessly into a convenient testing and development environment such as these tools of Typhoon (Typhon, Somerville, USA) [4] and dSPACE (dSPACE, Paderborn, Germany) [5]. It is possible to find testbed...

In recent years, interest in home energy management systems (HEMS) has grown significantly, as well as the development of Voice Assistants that substantially increase home comfort. This paper presents a novel merging of HEMS with the Assistant paradigm. The combination of both concepts has allowed the creation of a high-performance and easy-to-manage expert system (ES). It has been developed in a framework that includes, on the one hand, the efficient energy management functionality boosted with an Internet of Things (IoT) platform, where artificial intelligence (AI) and big data treatment are blended, and on the other hand, an assistant that interacts both with the user and with the HEMS itself. The creation of this ES has made it possible to optimize consumption levels, improve security, efficiency, comfort, and user experience, as well as home security (presence simulation or security against intruders), automate processes, optimize resources, and provide relevant information to the user facilitating decision making, all based on a multi-objective optimization (MOP) problem model. This paper presents both the scheme and the results obtained, the synergies generated, and the conclusions that can be drawn after 24 months of operation.

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Energy efficiency and consumers’ role in the energy system are among the strategic research topics in power systems these days. Smart grids (SG) and, specifically, microgrids, are key tools for these purposes. This paper presents a three-stage strategy for energy management in a prosumer nanogrid. Firstly, energy monitoring is performed and time-space compression is applied as a tool for forecasting energy resources and power quality (PQ) indices; secondly, demand is managed, taking advantage of smart appliances (SA) to reduce the electricity bill; finally, energy storage systems (ESS) are also managed to better match the forecasted generation of each prosumer. Results show how these strategies can be coordinated to contribute to energy management in the prosumer nanogrid. A simulation test is included, which proves how effectively the prosumers’ power converters track the power setpoints obtained from the proposed strategy.