The increasing demand for reliable AI models is also evident in Hierarchical Energy Management Systems (HEMSs). Especially in complex energy systems like Renewable Energy Communities (RECs), efficient cost minimization is a strong incentive for potential users to participate. Nevertheless, trust in AI stems not only from its performance but also from AI Explainability (XAI). In this regard, the new Kolmogorov-Arnold Network (KAN) XAI paradigm offers significant advantages over Multi-Layer Perceptrons (MLPs). In this work, a KAN is optimized by a Genetic Algorithm (GA) to serve as an inference engine in a realistic REC HEMS. The main goal is to validate KANs in that application domain. Secondly, a custom coefficient space quantization is proposed to enable efficient KAN-GA encoding. Specifically, the generic KAN model is encoded as a GA individual and optimized to minimize the operational cost of the REC. Then, an explainable AI model is extracted from the original KAN by fitting its connection splines with simpler function forms and by applying the Kolmogorov-Arnold Theorem to get the output. The results show a high precision of the KAN models with a low computational cost. In addition, the explainable model performance is very close to that of the original KAN in terms of precision, while being significantly better in terms of computational efficiency, with about 10% time-saving. Therefore, developing fast and explainable KAN-based models is worthwhile in that application field.

Accurate forecasting of energy time series is essential to support Energy Management Systems (EMS) in taking real time decisions of energy flows in microgrids. In the traditional two-stage methodology, forecasts are made upstream and provided as input to the EMS by a prediction model trained in advance on energy prices, loads and generation time series. This study adopts an innovative approach of Decision Focused Learning (DFL) for energy demand, production and price forecasting in a microgrid environment, wherein for each time series an LSTM neural network is trained end-to-end by embedding the optimization cost directly into the loss function. Results show that the proposed DFL approach reduces the operational cost by 11% compared to the conventional two-stage method.

Accurate energy consumption forecasting is critical for modern power systems, supporting both operational reliability and cost optimization. Deep learning approaches, particularly Long Short-Term Memory (LSTM) networks, have become increasingly prevalent in time-series load forecasting. However, typical LSTM-based solutions do not explicitly model inter-feature correlations and often undervalue peak-load periods, which are crucial for grid stability and energy market operations. This paper proposes a Graph-Aug LSTM (Graph-Augmented LSTM) with a weighted loss function. First, we construct a feature-level correlation graph and employ a Graph Attention Network (GAT) to learn a global embedding that captures cross-feature relationships. This embedding is concatenated at each LSTM time step, providing the model with explicit awareness of inter-feature dependencies. As an additional novelty, we introduce a Weighted Mean Squared Error (MSE) that emphasizes peak consumption intervals, thereby reducing the risk of underestimating high-demand periods. Validation on multiple U.S. city datasets demonstrates that our approach consistently outperforms naive baselines, a standard LSTM, and XGBoost in both overall error metrics and peak-load accuracy. These results highlight the value of integrating graph-based feature embeddings with a peak-focused loss function for more reliable and interpretable load forecasting.

Predictive maintenance is critical for ensuring the reliability and efficiency of Medium Voltage (MV) power grids. This paper presents a novel framework combining Kolmogorov–Arnold Networks (KANs) with SHapley Additive ex-Planations (SHAP) to predict and interpret real-world faults detected in Azienda Comunale Energia e Ambiente (ACEA)’s MV grid in Rome (Italy). The KAN model captures complex nonlinear interactions between Constitutive Parameters (CPs) and Exogenous Causes (ECs) measured through smart sensors, achieving high predictive performance with a ROC AUC of 0.993 over several traditional classifiers. SHAP analysis enhances interpretability, revealing ECs, such as mean and maximum currents, as dominant predictors, while CPs, including cable length and material composition, highlight structural vulnerabilities. Insights from clustering and dependence analyses enable targeted maintenance strategies by distinguishing fault scenarios driven by environmental or structural factors. This integrative approach bridges predictive accuracy and actionable interpretability – within the explainable AI (XAI) paradigm, providing a robust tool for smart grid management and condition-based maintenance.

Hierarchical processing and information granulation have proven essential for intelligent systems, as exemplified by Large Language Models (LLMs) backed by Transformer architectures, which leverage stacked attention modules to learn progressively richer semantic features. In this work, we offer an investigation of the role of attention layers in the hierarchy through a GPT-2 layer ablation methodology, which recalls the deactivation of the HAL 9000 computer modules in the iconic scene of the film "2001: A Space Odyssey". The adopted methodology is based on the measurement of appropriate indices (Dale-Chall Readability, BLEU and Text Flow – a measure of the coherence within the flow of sentences) characterizing the text produced following the removal of a combination of layers assisted by a single-way analysis, to characterize these combinations. Subsequently, through a "machine-in-the-loop" procedure, we let GPT-4 to judge the texts produced by GPT-2. The obtained results are in line with the basic hypothesis according to which the hierarchical organization of the Transformers is the ground of the high semantic performances, opening the path to further insights and application hypotheses such as Explainable AI and the analytical characterization of the texts produced by Generative AI models.

The current worldwide use of generative models in the field of Artificial Intelligence creates new possibilities but also new challenges and problems. In such an interconnected world, data produced by machines can become part of the training phases of new Language Models (LMs), and this can result in a problem that some scholars are starting to call "model collapse". In this paper, we discuss the problem from three points of view. Firstly, we show how the accumulation of data can cause a given probability distribution obtained through a sampling procedure to degenerate towards a single-state-like equilibrium as the iterations of accumulations go by. The second perspective concerns the analysis through semantic, syntactic and complexity theory-related measures of texts produced through a recursive procedure involving an LSTM that is driven by its own generated data. The aim is to measure the quality of the text and the possible level of degradation. The third point concerns the synthesis of a simplified differential model that frames this scenario in which the models feed themselves with their data and which allows do estimates, albeit rough, about the future. Computational results show a model drift and degradation of the text structure that evolves towards simplified forms from the lexical point of view and degraded in terms of destruction of long-term correlations (one of the main ways to generate meaning in a sentence). Ultimately, the study demonstrates that the problem exists and, although far in the future thanks to the power of the modern architectures underlying LLMs, should not be underestimated.

Renewable Energy Communities (RECs) have emerged as a promising solution to the intermittent nature of Renewable Energy Sources (RES). The stochastic nature of RES demands the presence of Energy Storage Systems (ESS) to increase self-consumption. Such systems, however, must be properly managed to enhance battery life, minimize wear costs, and ensure safe operating conditions. RECs face challenges such as low self-consumption and limited battery lifespan. This paper proposes an enhanced Soft Actor-Critic (SAC) algorithm tailored to optimize battery dispatch in RECs. Unlike standard SAC, which primarily relies on action clipping, our approach directly penalizes constraint violations within the agent’s objective, guiding it toward more feasible and profitable dispatch strategies. The method maintains optimal battery State of Charge (SoC), extends battery life, and maximizes economic returns from solar energy usage. Compared to the standard SAC model, our Lagrange-SAC approach achieves an 18.2% improvement in the mean Self-Sufficiency Ratio (SSR), significantly increasing the efficiency of solar energy utilization. These findings highlight the potential of advanced reinforcement learning techniques to enhance real-world energy systems, promote sustainable practices, and improve the resilience of energy infrastructures.

Accurate forecasting in time series data is crucial, especially in the energy sector, where prediction precision significantly influences decision-making and operational efficiency. This study investigates the efficacy of integrating second derivative data into forecasting models for energy consumption. We employ four distinct energy consumption time series datasets, each exhibiting varied characteristics and trends. The core of our methodology is the innovative incorporation of second derivative data to improve the accuracy of energy forecasting. This approach is applied to two widely recognized forecasting algorithms: Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost). Our research introduces the second derivative of energy data as an additional input for these algorithms. This supplementary feature is used to provide deeper insight into the acceleration and deceleration trends in energy consumption, aspects often overlooked in standard models. We compare the performance of these enhanced models against their traditional counterparts, which do not utilize second derivative data. The results demonstrate a significant improvement in forecasting accuracy, particularly in peak regions, for both LSTM and XGBoost models with the inclusion of second derivative data. This research holds broad practical applications, notably in energy management systems and smart grid technologies, where it can contribute to more efficient energy distribution decisions.

Smart Grids (SGs) represent a prominent energy efficiency strategy in the fight against Climate Change. In 2018, with RED II, the European Union made SGs realization more concrete thanks to the definition of Renewable Energy Communities (RECs), i.e. local electrical grids equipped with Renewable Energy Generators (REGs) and batteries. REC participants receive incentives proportionally to the green energy they share, aiming at more affordable technology. Therefore, proper Energy Management Systems (EMS) are required to minimize energy losses and costs to exploit accurate real-time power forecasting. In this context, battery management plays a crucial role because of its function as an energy buffer and its not negligible costs. That said, fast and accurate battery models are needed to synthesize realistic EMSs from the perspective of efficient embedding in the field. Equivalent Circuit Models (ECM) offer speed, reasonable accuracy and good explainability as grey-box models. In this work, a Thevenin ECM is synthesized with the aim of increasing realism in a REC EMS model, which relies on a simple battery linear model. The above ECM objective is to achieve flexibility and embedding suitability. First, one single trained model is synthesized for different ambient temperatures, and, secondly, promising results towards only one set of optimal parameters (instead of two, one for charging and one for discharging) are optimized, leading to possible future developments aiming at a faster and more general battery model for online EMS applications. The EMS results from simulations with the new ECM model are compared to the previous linear battery model, showing that the ECM model does not compromise the REC EMS thesis according to which auto-consumption is worse than a cost-minimization-oriented solution.

Modeling and recognizing events in complex systems through machine learning techniques is a challenging task. Especially if the model is constrained to be explainable and interpretable, while ensuring high levels of accuracy. In this paper, we adopt a bilinear logistic regression model in which the parameters are trained in a data-driven fashion on a real-world dataset of power grid failure data. The bilinear white-box model - grounded on a specific neural architecture - has been proven effective in classifying faulty states with a performance comparable to several classifiers in technical literature. Additionally, the low computational complexity of the bilinear model, in terms of the number of free parameters, allows gaining insights into the fault phenomenon correlating the events that impact the power grid (exogenous causes) with its constitutive characteristics, thence eliciting the relational information hidden in the data. The proposed model is also able to estimate a vulnerability vector that can be associated, as a suitable characteristic "label", to power grid components, opening the way, as will be deeply demonstrated in the following, not only to predictive maintenance programs or condition monitoring tasks but also to risk assessment and scenario analyses in line with the explainable AI paradigm.

The introduction of Transformer architectures - with the self-attention mechanism - in automatic Natural Language Generation (NLG) is a breakthrough in solving general task-oriented problems, such as the simple production of long text excerpts that resemble ones written by humans. While the performance of GPT-X architectures is there for all to see, many efforts are underway to penetrate the secrets of these black-boxes in terms of intelligent information processing whose output statistical distributions resemble that of natural language. In this work, through the complexity science framework, a comparative study of the stochastic processes underlying the texts produced by the English version of GPT-2 with respect to texts produced by human beings, notably novels in English and programming codes, is offered. The investigation, of a methodological nature, consists first of all of an analysis phase in which the Multifractal Detrended Fluctuation Analysis and the Recurrence Quantification Analysis - together with Zipf's law and approximate entropy - are adopted to characterize long-term correlations, regularities and recurrences in human and machine-produced texts. Results show several peculiarities and trends in terms of long-range correlations and recurrences in the last case. The synthesis phase, on the other hand, uses the complexity measures to build synthetic text descriptors - hence a suitable text embedding - which serve to constitute the features for feeding a machine learning system designed to operate feature selection through an evolutionary technique. Using multivariate analysis, it is then shown the grouping tendency of the three analyzed text types, allowing to place GTP-2 texts in between natural language texts and computer codes. Similarly, the classification task demonstrates that, given the high accuracy obtained in the automatic discrimination of text classes, the proposed set of complexity measures is highly informative. These interesting results allow us to add another piece to the theoretical understanding of the surprising results obtained by NLG systems based on deep learning and let us to improve the design of new informetrics or text mining systems for text classification, fake news detection, or even plagiarism detection.

Accurate energy consumption forecasting is essential for optimizing resource allocation and ensuring a reliable energy supply. This paper conducts a thorough analysis of energy consumption forecasting using XGBoost, SARIMA, LSTM, and Seasonal-LSTM algorithms. It utilizes two years of hourly electricity demand data from Italy and the PJM region (USA), categorizing algorithms into seasonality and non-seasonality groups. Performance metrics like RMSE, MAE, R2, and MSPE are employed. The study underscores the importance of considering seasonality, with SARIMA and Seasonal-LSTM achieving high accuracy in the seasonality group. In the non-seasonality group, XGBoost and LSTM perform competitively. In summary, this research aids in choosing suitable forecasting algorithms for building an Energy Management System for smart energy management in microgrids, considering seasonality and data attributes. These insights can also benefit energy companies in efficient resource management, promoting sustainable energy practices and urban development.

In the era of generalist social media, finding users who share the same diseases and the same related experiences during their course is one of the main objectives of patients. In this reference framework, in applications related to recommender systems or infoveillance, just to name a few, it is useful to synthesize language models capable of capturing the semantic relationships in short texts written by patients in various posts, with the dual goal of training well-performing classification systems. In this work, a series of semantic text representation approaches - both traditional and advanced - are compared through NLP techniques, in order to classify Italian users belonging to discussion groups on medical topics. The classification and semantic evaluation experiments of the models are satisfactory above all, especially by considering that the collected dataset is unbalanced.

The study of languages' structure and their organization in a set of well-defined relation schemes is a delicate matter. In the last decades, the convergence of traditional conflicting views by linguists is supported by an interdisciplinary approach that involves not only genetics or bio-archelogy but nowadays even the science of complexity. In light of this new and useful approach, this study proposes an in-depth analysis of the complexity underlying the morphological organization, in terms of multifractality and long-range correlations, of several modern and ancient texts pertaining to various linguistic strains (including ancient Greek, Arabic, Coptic, Neo-Latin and Germanic languages). The methodology is grounded on the mapping procedure between lexical categories belonging to text excerpts and time series, which is based on the rank of the frequency occurrence. Through the well-known MFDFA technique and a specific multifractal formalism, several multifractal indexes are then extracted for characterizing texts and the multifractal signature has been adopted for characterizing several language families, such as Indo-European, Semitic and Hamito-Semitic. The regularities and differences in the linguistic strains are assessed within a multivariate statistical framework and corroborated with a Machine Learning approach that is dedicated, in turn, to investigate the predictive power of the multifractal signature pertinent to text excerpts. The obtained results show a strong presence of persistence, i.e., memory, in the morphological structure of analyzed texts and we claim that this property has a role in characterizing the studied linguistic families. In fact, for example, the proposed analysis framework - grounded on complexity indexes - is able to easily distinguish ancient Greek texts from Arabic ones, as they belong to different language strains, i.e., indo-European and Semitic, respectively. The proposed approach has been proven effective and can be adopted for further comparative studies and for designing new informetrics for further advances in the fields of information retrieval and Artificial Intelligence.

The adoption of autonomous driving systems is increasingly widespread in land vehicles for private and public transportation and more standards have been defined (among which SAE and ADAS are the most complete). Although in maritime transportation automatic navigation was developed earlier with respect to similar systems developed for land vehicles, there are no standards equivalent to ADAS and SAE. Furthermore, the automation on boats is always partial and refers only to the route planning, obstacle avoiding and motion control functions. In this paper an autonomous driving system in a simulated environment for boats is presented with the attempt to help to define a standard equivalent to those mentioned above. The autonomous driving system presented takes into consideration many simulation aspects and has been developed as a single complete software solution. The design involved both classical approaches and computational intelligence techniques. We distinguish three main implementation phases relating to as many levels of control of the architecture. In this paper, we present only the simulation results related to the mid-level, giving particular attention to the tasks of boat avoidance and docks avoidance (i.e., entry and exit from ports), in which the differences between the crisp and fuzzy logic versions of the same mid-level controllers are highlighted. Results show how fuzzy controllers allow ADS to achieve a lower probability of collision and stall with the same performance as an ADS with crisp controllers. The performance indicator that has been formulated for this work is inspired by Fish Schooling Behavior, which has strong similarities with the self-driving boat problem.

Even though it is known that Renewable Energy Sources (RESs) are necessary to face Climate Change and pollution, technology is still in a developement phase, aiming at improving energy exploitation from RESs, as these type of sources suffer from low energy density and variability over time. Thus, proper ICT infrastructures equipped with a robust software, i.e., Energy Management System (EMS), are needed to ensure that Renewable Energy (RE) does not go to waste. Relatively small local electrical grids called Microgrids (MGs) represent the EMS ecosystem, since their main features are the proximity between generation and loads and the presence of Energy Storage Systems (ESSs) adopted to recover surplus energy. The Vehicle-to-Grid (V2G) paradigm helps to realize the Smart City, which in substance is an interconnection of MGs hosting electrical vehicles for an efficient energy management at a larger scale. In this context, e-boats have only recently been considered. Hence, in this work a Multi-Objective (MO) EMS is synthesized for an e-boat docked in a small Microgrid (PV generator and ESS) with the aim of maximizing the charging time of the e-boat ESS and spending as little as possible both for energy purchase and also in terms of ESS wear. A Fuzzy Inference System - Hierarchical Genetic Algorithm (FIS-HGA) is used to achieve the Pareto Front, with the HGA that is in charge of optimizing the FIS parameters. Results laid to a balanced trade-off between the two objectives, since the e-boat ESS is almost fully charged in a reasonable time and with a low cost, compatible with people transportation. Last but not least, the inference process of a FIS is easily interpretable, in the perspective of an Explainable AI.

The ability to perform predictive maintenance, as one of the main asset of Industry 4.0, is known to help improve downtime, costs, control and production quality. Modern predictive maintenance programs involve machine learning techniques, within the AI umbrella, that work in a data-driven fashion. This is true in all machinery where, through intelligent sensors, it is possible to collect data to be processed to detect faults or carry out anomaly detection activities. This paper presents a system for the detection of anomalies in the railway context and, specifically, in the pressurization systems of Italian high-speed trains. The available real-world dataset is in form of unlabeled time series of fixed length of 600 samples. Hence, it is proposed a two-stage machine learning workflow where the first stage acts in an unsupervised fashion through a statistical technique validated by field experts with the aim of building a labeled dataset. In the second stage, the faced problem is conceived as a classification task in the context of a strong class imbalance problem - very likely in predictive maintenance - where are compared two feature engineering techniques. The first one considers directly the raw signals as input of a SVM algorithm. In the second, time series are subjected to an adaptive heuristic procedure of piece-wise approximation, whose output is a sequence of$\mathbb{R}^{2}$vectors (slopes and intercepts). In this case, the classification task is carried out in the so-called “dissimilarity space” for pattern recognition adopting different dimensions of the representation set obtained through a clustering algorithm. The dissimilarity measure consists of an ad-hoc edit distance capable of measuring the dissimilarity between 2-dimensional sequences. In this study a k-medoids clustering procedure is adopted for balancing the dataset together with further additional techniques for solving the challenging problem of unbalanced data, offering a deep comparison related to various experimental methodologies.

Abstract Machine Learning is currently a well-suited approach widely adopted for solving data-driven problems in predictive maintenance. Data-driven approaches can be used as the main building block in risk-based assessment and analysis tools for Transmission and Distribution System Operators in modern Smart Grids. For this purpose, a suitable Decision Support System should be able of providing not only early warnings, such as the detection of faults in real time, but even an accurate probability estimate of outages and failures. In other words, the performance of classification systems, at least in these cases, needs to be assessed even in terms of reliable outputting posterior probabilities, a really important feature that, in general, classifiers very often do not offer. In this paper are compared several state-of-the-art calibration techniques along with a set of simple new proposed techniques, with the aim of calibrating fuzzy scoring values of a custom-made evolutionary-cluster-based hybrid classifier trained on a set of a real-world dataset of faults collected within the power grid that feeds the city of Rome, Italy. Comparison results show that in real-world cases calibration techniques need to be assessed carefully depending on the scores distribution and the proposed techniques are a valid alternative to the ones existing in the technical literature in terms of calibration performance, computational efficiency and flexibility.

This paper describes the architecture and control design of an autonomous Electric Boat, together with a specific simulation environment for training and testing the Fuzzy Inference Systems. The boat will be in charge to exit and enter from harbors, plan and follow a route, avoid obstacles such as other boats, correct its motion, perform a virtual anchor and switch between these operations autonomously. The boat is equipped with a set of smart sensors such as sonars, a Global Positioning System, a camera-based vision system and an Inertial Measurement Unit. General navigation rules are respected during the route. We propose an architecture integrating several Fuzzy Controller-based modular pipelines. Furthermore, we propose a mathematical formalization of the Fish Schooling Behavior useful for training Fuzzy Controllers through Q-Learning. Our architecture will soon be implemented on a real boat intended for navigating in inland waters.

Text mining and text classification are gaining more and more importance in AI related research fields. Researchers are particularly focused on classification systems, based on structured data (such as sequences or graphs), facing the challenge of synthesizing interpretable models, exploiting gray-box approaches. In this paper, a novel gray-box text classifier is presented. Documents to be classified are split into their constituent words, or tokens. Groups of frequent m tokens (or m-grams) are suitably mined adopting the Granular Computing framework. By fastText algorithm, each token is encoded in a real-valued vector and a custom-based dissimilarity measure, grounded on the Edit family, is designed specifically to deal with m-grams. Through a clustering procedure the most representative m-grams, pertaining the corpus of documents, are extrapolated and arranged into a Symbolic Histogram representation. The latter allows embedding documents in a well-suited real-valued space in which a standard classifier, such as SVM, can safety operate. Along with the classification procedure, an Evolutionary Algorithm is in charge of performing features selection, which is able to select most relevant symbols – m-grams – for each class. This study shows how symbols can be fruitfully interpreted, allowing an interesting knowledge discovery procedure, in lights with the new requirements of modern explainable AI systems. The effectiveness of the proposed algorithm has been proved through a set of experiments on paper abstracts classification and SMS spam detection.

Modeling and predicting failures in the field of predictive maintenance is a challenging task. An important issue of an intelligent predictive maintenance system, exploited also for Condition Based Maintenance applications, is the failure probability estimation that can be found uncalibrated for most standard and custom classifiers grounded on Machine learning. In this paper are compared two classification techniques on a data set of faults collected in the real-world power grid that feeds the city of Rome, one based on a hybrid evolutionary-clustering technique, the other based on the well-known Gaussian Mixture Models setting. While the former adopts directly a custom-based weighted dissimilarity measure for facing unstructured and heterogeneous data, the latter needs a specific embedding technique step performed before the training procedure. Results show that both approaches reach good results with a different way of synthesizing a model of faults and with different structural complexities. Furthermore, besides the classification results, it is offered a comparison of the calibration status of the estimated probabilities of both classifiers, which can be a bottleneck for further applications and needs to be measured carefully

Multi-agent systems recently gained a lot of attention for solving machine learning and data mining problems. Furthermore, their peculiar divide-and-conquer approach is appealing when large datasets have to be analyzed. In this paper, we propose a multi-agent classification system able to tackle large datasets where each agent independently explores a random small portion of the overall dataset, searching for meaningful clusters in proper subspaces where they are well-formed (i.e., compact and populated). This search is orchestrated by means of a genetic algorithm able to act in a multi-modal fashion, since meaningful clusters might lie in different subspaces. Furthermore, since agents operate independently one another, their execution is parallelized across different computational units. Tests show that the proposed algorithm, E-ABC2, is able to deal with large datasets, returning satisfactory results in terms of scalability and performances, especially when compared with our previous baseline versions.

Natural language processing and text mining applications have gained a growing attention and diffusion in the computer science and machine learning communities. In this work, a new embedding scheme is proposed for solving text classification problems. The embedding scheme relies on a statistical assessment of relevant words within a corpus using a compound index originally proposed in ecology: this allows to spot relevant parts of the overall text (e.g., words) on the top of which the embedding is performed following a Granular Computing approach. The employment of statistically meaningful words not only eases the computational burden and the embedding space dimensionality, but also returns a more interpretable model. Our approach is tested on both synthetic datasets and benchmark datasets against well-known embedding techniques, with remarkable results both in terms of performances and computational complexity.

Calibrating a classification system consists in transforming the output scores, which somehow state the confidence of the classifier regarding the predicted output, into proper probability estimates. Having a well-calibrated classifier has a non-negligible impact on many real-world applications, for example decision making systems synthesis for anomaly detection/fault prediction. In such industrial scenarios, risk assessment is certainly related to costs which must be covered. In this paper we review three state-of-the-art calibration techniques (Platt’s Scaling, Isotonic Regression and SplineCalib) and we propose three lightweight procedures based on a plain fitting of the reliability diagram. Computational results show that the three proposed techniques have comparable performances with respect to the three state-of-the-art approaches.

Due to the increasing amount of sensors and data streams that can be collected in order to monitor electric distribution networks, developing predictive diagnostic systems over Smart Grids demands powerful and scalable algorithms in order to search for regularities in Big Data. In this regards, Evolutive Agent Based Clustering (E-ABC) is a promising framing reference, as it is conceived to orchestrate a swarm of intelligent agents acting as individuals of an evolving population, each performing a random walk on a different subset of patterns. Each agent is in charge of discovering well-formed (compact and populated) clusters and, at the same time, a suitable subset of features corresponding to the subspace where such clusters lie, following a local metric learning approach, where each cluster is characterized by its own subset of relevant features. E-ABC is able to process data belonging to structured and possibly non metric spaces, relying on custom parametric dissimilarity measures. In this paper, a supervised version of E-ABC is proposed. This novel classification system has been employed for recognizing and predicting localized faults on the electric distribution network of Rome, managed by the Italian utility company ACEA. Tests results show that E-ABC is able to synthesize classification models characterized by a remarkable generalization capability, with adequate performances to be employed in Smart Grids condition based management systems. Moreover, the feature subsets where most of the meaningful clusters have been discovered can be used to better understand sub-classes of failures, each identified by a set of related causes.

Dissimilarity spaces, along with feature reduction/ selection techniques, are among the mainstream approaches when dealing with pattern recognition problems in structured (and possibly non-metric) domains. In this work, we aim at investigating dissimilarity space representations in a biology-related application, namely protein function classification, as proteins are a seminal example of structured data given their primary and tertiary structures. Specifically, we propose two different analyses relying on both the complete dissimilarity matrix and a dimensionally-reduced version of the complete dissimilarity matrix, thereby casting the pattern recognition problem from structured domains towards real-valued feature vectors, for which any standard classification algorithm can be used. A third, hybrid, analysis uses a clustering-based one-class classifier exploiting different representations. First results conducted on a subset of the Escherichia coli proteome are promising and some of the analyses presented in this work may also dually suit field-experts, further bridging the gap between natural sciences and computational intelligence techniques.

In this paper we present an interesting application of the Decision Support System, known as the OCC_System, designed for faults recognition and classification within the real-world Medium Voltage power grid of Rome, Italy, managed by the Azienda Comunale Energia e Ambiente (ACEA) company. Given a historical data set consisting of fault patterns described by heterogeneous features related to endogenous and exogenous factors, the recognition system is trained to classify fault states assigning them a probability of fault. Disambiguating the external causes, whose dynamic is fast, and the constitutive parameters of the power grid, whose dynamic is slow, an affine algebraic model is estimated through an evolutionary technique in order to obtain a vulnerability index related to power grid equipment. The estimation procedure allows obtaining even a correlation matrix among external causes and constitutive parameters useful to better characterize the fault phenomenon under study.

The worldwide power grid can be thought as a System of Systems deeply embedded in a time-varying, non-deterministic and stochastic environment. The availability of ubiquitous and pervasive technology about heterogeneous data gathering and information processing in the Smart Grids allows new methodologies to face the challenging task of fault detection and modeling. In this study, a fault recognition system for Medium Voltage feeders operational in the power grid in Rome, Italy, is presented. The recognition task is performed synthesizing a data-driven model of fault phenomenons based on a hybridization of Evolutionary learning and Clustering techniques. The model is synthesized starting from a set of clusters obtained by partitioning the fault patterns, tuning at the same time the core dissimilarity measure. In this paper we show as clusters can be successively analyzed for mining useful information about the fault phenomenon and to build up an ad-hoc decision system to support business strategies such as Condition Based Maintenance tasks.

This paper presents a novel power flow optimization strategy in Micro Grids (MGs) connected to the main grid. When the MG includes stochastic energy sources, such as photovoltaic and micro eolic-generators, it is very useful to rely on Energy Storage Systems (ESSs) to buffer energy. In fact, an ESS can be employed to perform several functionalities, related to different user requirements, such as power stability, peak shaving, optimal energy trading, etc. The Energy Management System is based on a Fuzzy Logic Controller (FLC) optimized by a Multi-Objective Genetic Algorithm in order to maximize both the total profit in energy trading with the main grid and the State of Health (SOH) of the ESS. The FLC manages the neat aggregate energy deficit and surplus inside the MG, analyzing in real time the state of the MG (aggregated energy demand and production, State of Charge of the ESS, energy sale and purchase prices). The FLC is tested on a MG composed by a photovoltaic solar generator, a domestic user and a Li-ion battery. A multi-objective genetic algorithm is in charge to find the set of solutions on the Pareto front. The results are compared with the same FLC optimized by a mono-objective Genetic Algorithm (GA) minimizing in a first case only the total profit and in the second case a convex linear combination of the total profit and a measure of the battery stress.

This paper presents a novel power flow optimization strategy for a Grid Connected microgrid (MG) equipped with a Battery Energy Storage System (BESS), namely a Li-Ion battery pack. A BESS can be employed to perform several functionalities, related to different user requirements, such as power stability, peak shaving, optimal energy trading, etc. In the proposed system the MG is composed by an aggregation of distributed power generators and loads and a BESS is adopted to manage the power over-production/over-demand in real time, in order to maximize the prosumer profit looking at the current energy prices and the BESS State of Charge (SOC). The Energy Management System (EMS) is based on a Fuzzy Logic Controller (FLC) with a suitable rule inference system designed by an Expert Operator (EO). The control strategy is tested with different power profiles and BESS capacities in order to verify its effectiveness and limits. Furthermore, the FLC has been optimized by a Genetic Algorithm to increase the total profit exploiting the BESS as energy buffer. The optimization results have been compared to the initial FLC designed by the EO, taking into account both the profit and the deterioration of the BESS measured through a suitable battery stress index.

The analysis and recognition of fault status in the Smart Grid field is a challenging problem. Computational Intelligence techniques have already been shown to be a successful framework to face complex problems related to a Smart Grid. The availability of huge amounts of data coming from smart sensors allows the system to take a fine grained picture of the power grid status. This data can be processed in order to offer an instrument in aiding humans operators to better understand the power grid status and to take decisions on grid operations. This paper addresses the problem of fault recognitions in a real-world power grid (i. e. the power grid that feds the city of Rome, Italy) with the One-Class Classification paradigm by a combined approach of dissimilarity measure learning by means of an evolution strategy and clustering techniques for modeling the decision regions between fault status and the standard functioning of the power system. In this paper we present an in-depth study of the performance of two clustering algorithms in building up the model of faults, as the core procedure of the proposed recognition system.

Due to the intrinsic complexity of real-world power distribution lines, which are highly non-linear and time-varying systems, modeling and predicting a general fault instance is a very challenging task. Power outages can be experienced as a consequence of a multitude of causes, such as damage of some physical components or grid overloads. Smart grids are equipped with sensors that enable continuous monitoring of the grid status, hence allowing the realization of control systems related to different optimization tasks, which can be effectively faced by Computational Intelligence techniques. This paper deals with the problem of faults modeling and recognition in a real-world smart grid, located in the city of Rome, Italy. It is proposed a suitable classication system able to recognize faults on medium voltage feeders. Due to the nature of the available data, the one-class classication framework is adopted. Experiments are presented and discussed considering a three-year period of measurements of fault events gathered by ACEA Distribuzione S.p.A., the company that manages the smart grid system under analysis. Results demonstrate the effectiveness and validity of our approach.