Battery Cell design and control have been widely explored through modeling and simulation. On the one hand, Doyle’s pseudo-two-dimensional (P2D) model and Single Particle Models are among the most popular electrochemical models capable of predicting battery performance and therefore guiding cell characterization. On the other hand, empirical models obtained, for example, by Machine Learning (ML) methods represent a simpler and computationally more efficient complement to electrochemical models and have been widely used for Battery Management System (BMS) control purposes. This article proposes ML-based ensemble models to be used for the estimation of the performance of an LIB cell across a wide range of input material characteristics and parameters and evaluates 1. Deep Learning ensembles for simulation convergence classification and 2. structured regressors for battery energy and power predictions. The results represent an improvement on state-of-the-art LIB surrogate models and indicate that deep ensembles represent a promising direction for battery modeling and design.

With the increasing amount of devices connected to the huge net known as the internet, it is not surprising the corresponding growth of cyber attacks. The era of the Internet of Things (IoT) has proved to be an environment in which malicious activities have been able to proliferate with great comfort. Due to this kind of threat, having powerful machine learning-based classifiers is almost a critical need nowadays. Besides, to properly face new and previously unseen attacks unsupervised learners need to be involved. An unsupervised network threat detector is provided in this work. The proposed approach is based on three steps. The main one is a novel anomaly score, which relies on modeling the tails of the empirical distributions and on an interpretation of the well-known Bayes theorem. A step for an unsupervised feature selection and another one for data reduction are also considered, both aimed at gaining robustness of the procedure. All three steps are in a completely unsupervised way, facilitating an ad hoc scenario deploying. The whole method showed good performance reaching \(98.44\%\) and \(98.14\%\) in the F1-score over different datasets. The obtained results are competitive with other state-of-the-art methods.

Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and residential consumers through customer clusterisation, tailoring the forecasting models to capture the unique consumption patterns of each group. Feature selection incorporated temporal, socio-economic, and weather-related data obtained from the Copernicus Earth Observation (EO) program. A variety of AI and machine learning algorithms for short-term load forecasting (STLF) and very-short-term load forecasting (VSTLF) are explored and compared, determining the most effective approaches. With all that, the main contribution of this work are the new forecasting approaches proposed, which have demonstrated superior performance compared to simpler models, both for STLF and VSTLF, highlighting the importance of customized forecasting strategies for different consumer groups and demonstrating the impact of incorporating detailed weather data on forecasting accuracy. These advancements contribute to more reliable power demand predictions, with our novel forecasting approaches reducing the Mean Absolute Percentage Error (MAPE) by up to 1–3% for industrial and 1–10% for commercial consumers compared to baseline models, thereby supporting grid stability.

Detecting changes or shifts in data over time is known as change-point detection. This phenomenon is crucial for identifying the deterioration of industrial components and preventing costly breakdowns or failures. There are several supervised and unsupervised approaches used in change-point detection, which involve evaluating the difference between the sampling distributions of two-time windows. The accurate detection of change-points is a critical challenge addressed by Industry 4.0 and can enable timely action to avoid costly failures in industrial elements. This paper discusses the use of distance-based common spatial patterns (DB-CSPs) as an offline change-point detection technique in multivariate time series data. DB-CSP is a supervised approach that projects the data into a subspace to identify the most relevant features that differentiate between two-time windows. Afterward, a classification algorithm is used to effectively detect changes in the data. We demonstrate the adequacy of LynxSight using public datasets and apply the new approach to a real industrial use case, achieving better results than some state-of-the-art techniques.