Energy consumption in the residential sector represents a global average of around 30% of the total consumed in the country, so any improvement that could reduce it, would have significant effects. Some of these upgrades are the incorporation of advanced communication and control algorithms based on predictive models of household consumption. This article describes the design, development and application of a fully centralized system in which home appliances are managed by a wireless sensor network. The management algorithms are based on consumption models in combination with timing schedule, power, temperature or ambient light measurements and prioritization. On the control side, on-off management is basically presented although DALI protocol for lighting continuous regulation is now implemented too. Finally it presents future work in this field.

Cyber–physical systems involve the creation, continuous updating, and monitoring of virtual replicas that closely mirror their physical counterparts. These virtual representations are fed by real-time data from sensors, Internet of Things (IoT) devices, and other sources, enabling a dynamic and accurate reflection of the state of the physical system. This emphasizes the importance of data synchronization, visualization, and interaction within virtual environments as a means to improve decision-making, training, maintenance, and overall operational efficiency. This paper presents a novel approach to a cyber–physical system that integrates virtual reality (VR)-based digital twins and 3D SCADA in the context of Industry 4.0 for the monitoring and optimization of an olive mill. The methodology leverages virtual reality to create a digital twin that enables immersive data-driven simulations for olive mill monitoring. The proposed CPS takes data from the physical environment through the existing sensors and measurement elements in the olive mill, concentrates them, and exposes them to the virtual environment through the Open Platform Communication United Architecture (OPC-UA) protocol, thus establishing bidirectional and real-time communication. Furthermore, in the proposed virtual environment, the digital twin is interfaced with the 3D SCADA system, allowing it to create virtual models of the process. This innovative approach has the potential to revolutionize the olive oil industry by improving operational efficiency, product quality, and sustainability while optimizing maintenance practices.

Due to the myriad of loads that are collected into commercial grid-interactive efficient buildings (GEBs) focused on the industry 4.0 paradigm, it is important to ensure their proper electrical operation. The power quality (PQ) here requires a granular monitoring approach, reaching a point where each device connected to the microgrid can diagnose whether its power supply is optimal. Otherwise, it can participate cooperatively in decision-making to avoid anomalies or faults in the microgrid. In this work, we present cloud-based extended functionality to make smart appliances responsive to the grid, either autonomously or managed under the open automated demand response standard. Further to acting as a switch, the main strength lies in its PQ monitoring via the FIWARE Internet of Things (IoT) platform with data-driven analytics capabilities. It identifies and even predicts a broad spectrum of electrical disturbances, far exceeding the capabilities of previous solutions such as the grid-friendly appliance controller, so it is possible to customize a battery of alarms at will (e.g., according to IEEE 1547 standard). Moreover, although it can act autonomously, its main mission will be to act in a coordinated manner, either cooperatively or under the supervision of the GEB Energy Management System. Finally, different case studies are presented to show their capabilities. With the integration of these distributed submetering systems, under standards IoT wireless communication protocols, a further step will be taken in the advent of the digital utility paradigm.

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.

Smart Grid refers to the next generation power grid in which the power management is upgraded by incorporating advanced communications and capabilities for improved control and efficiency. Among all fields covered by the term Smart Grid energy management in home and building facilities is one of the aspects which is still not really developed. Our main goal is the creation of an application to manage the power consumption at home by controlling household appliances and other loads by means of setting the maximum power suitable and a priority algorithm based on timing schedule, temperature or ambient light.

The massive proliferation of distributed energy resources (DERs) is changing the opportunities and challenges encountered in the power grid. Some technical barriers here include managing energy sources with high variability, loss of inertia or the occurrence of voltage disturbances. However, the growth of grid-connected inverter-based resources (IBRs) can also help address other vulnerabilities such as the presence of harmonic distortion. Therefore, apart from participating in the frequency and voltage regulation, IBRs can also exploit their capability of compensating current harmonics in the emerging harmonic power market (HPM). Nevertheless, Distribution System Operators' (DSOs) planning under this market becomes unrealistic without using forecasting tools. While deep learning (DL) approaches have been applied in the field of power quality (PQ), significantly less attention has been devoted to PQ time series forecasting, particularly in the case of current harmonics. Unlike all research to date, the present paper uses DL architectures to model multivariate time series (MTS) of the magnitude and phase angle of current harmonics computed according to the IEC 61000-4-30 and 61000-4-7 standards and the prevailing phasor methodology using a 10-minute interval. Measurements were performed at the point of connection (PoC) of the main data center building at the University of Cordoba, in southern Spain. Finally, absolute and relative error metrics were employed to assess the performance of the models, showing that the bidirectional long-short-term memory (BiLSTM) achieves the best results. The prediction error was also characterized to provide a confidence interval to drive optimization problems considering the magnitude and phase angle uncertainty.