The ever-increasing number of smart devices connected to the internet poses an unprecedented security challenge. This article presents the implementation of an Intrusion Detection System (IDS) based on the deployment of different one-class classifiers to prevent attacks over the Internet of Things (IoT) protocol Message Queuing Telemetry Transport (MQTT). The utilization of real data sets has allowed us to train the one-class algorithms, showing a remarkable performance in detecting attacks.

The significant advance of Internet of Things in industrial environments has provided the possibility of monitoring the different variables that come into play in an industrial process. This circumstance allows the supervision of the current state of an industrial plant and the consequent decision making possibilities. Then, the use of anomaly detection techniques are presented as a powerful tool to determine unexpected situations. The present research is based on the implementation of one‐class classifiers to detect anomalies in two industrial systems. The proposal is validated using two real datasets registered during different operating points of two industrial plants. To ensure a better performance, a clustering process is developed prior the classifier implementation. Then, local classifiers are trained over each cluster, leading to successful results when they are tested with both real and artificial anomalies. Validation results present in all cases, AUC values above 90%.

In electric vehicles and mobile electronic devices, batteries are one of the most critical components. They work by using electrochemical reactions that have been thoroughly investigated to identify their behavior and characteristics at each operating point. One of the fascinating aspects of batteries is their complicated behavior. The type of power cell reviewed in this study is a Lithium Iron Phosphate LiFePO${}_4$ (LFP). The goal of this study is to develop an intelligent model that can forecast the power cell State of Charge (SOC). The dataset used to create the model comprises all the operating points measured from an actual system during a capacity confirmation test. Regression approaches based on Deep Learning (DL), such as Long Short-Term Memory networks (LSTM), were evaluated under different model configurations and forecasting horizons.

This paper aims to enhance security in IoT device networks through a visual tool that utilizes three projection techniques, including Beta Hebbian Learning (BHL), t-distributed Stochastic Neighbor Embedding (t-SNE) and ISOMAP, in order to facilitate the identification of network attacks by human experts. This work research begins with the creation of a testing environment with IoT devices and web clients, simulating attacks over Message Queuing Telemetry Transport (MQTT) for recording all relevant traffic information. The unsupervised algorithms chosen provide a set of projections that enable human experts to visually identify most attacks in real-time, making it a powerful tool that can be implemented in IoT environments easily.

In recent years, growing concern about climate change and the need to reduce greenhouse gas emissions have highlighted the role of energy efficiency and sustainability on the global agenda. Energy policies are decisive in establishing regulatory frameworks and incentives to address these challenges, leading to an inclusive and more resilient energy transition. In this context, geothermal energy is an essential source of renewable, low-emission energy, capable of providing heat and electricity sustainably. The present research focuses on a bioclimatic house’s geothermal energy system based on a heating pump and a horizontal heat exchanger. The main aim is to predict the generated thermal power of the heat pump using historical data from several sensors. In particular, two approaches were proposed with both uni-variate and multi-variate scenarios. Several deep learning techniques were applied: LSTM, GRU, 1D-CNN, CNN-LSTM, and CNN-GRU, obtaining satisfactory results over the whole dataset, which comprised one year of data acquisition. Specifically, promising results have been achieved using hybrid methods combining recurrent-based and convolutional neural networks.

Energy storage plays a key role in the modern global economy, in which variable renewable energies are growingly acquiring a major part. One option for energy storage is the production of hydrogen through electrolysis of water with renewable electricity, which can later be used again to produce electricity in a fuel cell, internal combustion engine or gas turbine, among other applications. Renewable hydrogen is quickly approaching economic competitiveness and enjoying unprecedented political and business momentum, with the number of favorable policies and projects worldwide expected to increase rapidly in the coming years. The rising interest in this storage alternative is driven by the urgency of greenhouse gas emission mitigation, by the falling costs of renewable power and by systems integration challenges due to rising shares of variable renewable power supply. This book aims at reviewing the different available technologies for hydrogen production, storage and final use as an energy vector.