Energy efficiency is one of the most relevant issues that the scientific community, and society in general, must face in the next years. Furthermore, higher energy efficiencies will contribute to worldwide sustainability. Buildings are responsible for 40% of the overall consumed energy. Smart Grids and Smart Buildings are playing an essential role in the definition of the next generation of sustainable Smart Cities. The main goal is reducing the impact of energy consumption on the environment as much as possible. This paper focuses on information communication technologies (ICTs) and techniques, their key characteristics and contribution to obtain higher energy efficiencies in smart buildings. Given that electrical energy is the most used, the investigation mainly centres on this energy. This paper also pays attention to green energies and energy harvesting due to their contribution to energy efficiency by providing additional clean energy. The main contribution of this investigation is pointing out the most relevant existing and emerging ICT technologies and techniques which can be used to optimize the energy efficiency of Smart Buildings. The research puts special attention on available, novel and emerging sensors, communication technologies and standards, intelligence techniques and algorithms, green energies and energy harvesting. All of them enable high-performance intelligent systems to optimize energy consumption and occupants’ comfort. Furthermore, it remarks on the most suitable technologies and techniques, their main features and their applications in Smart Buildings.

Electronic devices using the piezoelectric effect contain piezoelectric materials: often crystals, but in many cases poled ferroelectric ceramics (piezoceramics), polymers or composites. On the one hand, these materials exhibit non-negligible losses, not only dielectric, but also mechanical and piezoelectric. In this work, we made simulations of the effect of the three types of losses in piezoelectric materials on the impedance spectrum at the resonance. We analyze independently each type of loss and show the differences among them. On the other hand, electrical and electronic engineers include piezoelectric sensors in electrical circuits to build devices and need electrical models of the sensor element. Frequently, material scientists and engineers use different languages, and the characteristic material coefficients do not have a straightforward translation to those specific electrical circuit components. To connect both fields of study, we propose the use of accurate methods of characterization from impedance measurements at electromechanical resonance that lead to determination of all types of losses, as an alternative to current standards. We introduce a simplified equivalent circuit model with electrical parameters that account for piezoceramic losses needed for the modeling and design of industrial applications.

Energy efficiency is a major concern to achieve sustainability in modern society. Smart cities sustainability depends on the availability of energy-efficient infrastructures and services. Buildings compose most of the city, and they are responsible for most of the energy consumption and emissions to the atmosphere (40%). Smart cities need smart buildings to achieve sustainability goals. Building’s thermal modeling is essential to face the energy efficiency race. In this paper, we show how ICT and data science technologies and techniques can be applied to evaluate the energy efficiency of buildings. In concrete, we apply machine learning techniques to classify buildings based on their energy efficiency. Particularly, our focus is on single-family buildings in residential areas. Along this paper, we demonstrate the capabilities of machine learning techniques to classify buildings depending on their energy efficiency. Moreover, we analyze and compare the performance of different classifiers. Furthermore, we introduce new parameters which have some impact on the buildings thermal modeling, especially those concerning the environment where the building is located. We also make an insight on ICT and remark the growing relevance in data acquisition and monitoring of relevant parameters by using wireless sensor networks. It is worthy to remark the need for an appropriate and reliable dataset to achieve the best results. Moreover, we demonstrate that reliable classification is feasible with a few featured parameters.

This letter describes a power amplifier (PA) with high linearity based on the envelope elimination and restoration (EER) technique. PAs based on this technique are composed of a highly efficient nonlinear PA (class E) that performs phase modulation and a wide bandwidth envelope amplifier that supplies the nonlinear PA and has to inject the envelope through power supply modulation. The design of the envelope amplifier is a complicated task due to the tradeoff between demanded high efficiency and very wide bandwidth. In order to obtain a reasonably high efficiency and very wide bandwidth, a solution based on the multilevel converter in series with a linear regulator is proposed. It provides up to 100 W of peak power and tracks sine waves up to 2 MHz. The class E amplifier has been optimized for high-efficiency (near 85%) and high-frequency (120 MHz) operation. The experimental results presented in this letter validate the proposed solutions for the envelope and phase amplifier, demonstrate that the implemented EER PA can handle up to 100 W of peak power, and highlight its high linearity (around 40 dB of attenuation of the intermodulation products) and high efficiency (up to 25% less power losses than an ideal class B amplifier).

The growing prevalence of cybersecurity threats is a significant concern for railway systems, which rely on an extensive network of onboard and trackside sensors. These threats have the potential to compromise the safety of railway operations and the integrity of the railway infrastructure itself. This paper aims to examine the current cybersecurity measures in use, identify the key vulnerabilities that they address, and propose solutions for enhancing the security of railway infrastructures. The report evaluates the effectiveness of existing security protocols by reviewing current standards, including IEC62443 and NIST, as well as case histories of recent rail cyberattacks. Significant gaps have been identified, especially where modern and legacy systems need to be integrated. Weaknesses in communication protocols such as MVB, CAN and TCP/IP are identified. To address these challenges, the paper proposes a layered security framework specific to railways that incorporate continuous monitoring, risk-based cybersecurity modeling, AI-assisted threat detection, and stronger authentication methodologies. The aim of these recommendations is to improve the resilience of railway networks and ensure a safer, more secure infrastructure for future operations.