Efficient energy management is critical for industrial consumers aiming to minimize operational costs while maintaining reliability. The electricity costs are crucial for companies’ competitiveness, and their reduction is important for the sector’s sustainability. This paper presents an optimization framework for determining the optimal contracted power for industrial energy consumers in Spain, leveraging historical smart meter data and available supply contract data. The proposed model evaluates historical consumption and billing structures to optimize contracted power, balancing fixed contracted power costs and penalization costs due to missing the optimal point. The study highlights significant cost savings, up to around 7

The growing dependence on collaborative robots in essential industrial and service sectors raises urgent concerns regarding their reliability and ability to handle faults. Undetected software issues can degrade performance, jeopardize safety, and result in expensive downtimes. Incorporating collaborative robots into daily life and industrial settings requires strong and dependable systems, especially concerning software. While most anomaly detection research has focused on hardware anomalies, this study addresses the underexplored challenge of software anomaly detection in component-based robotic systems. Leveraging a publicly available dataset with labeled software-induced anomalies, six one-class classification techniques were evaluated: Approximate Convex Hull, Autoencoder Neural Networks, K-Means, K-Nearest Neighbors, Principal Component Analysis, and Support Vector Data Description. Each classifier was assessed across preprocessing methods and hyperparameter configurations, using the Area Under the Curve (AUC) as the primary performance metric. The results show that Principal Component Analysis outperforms other methods in most scenarios, although the optimal performance varies depending on the anomaly type. The results confirm that the suggested one-class classification method is an efficient means of early identification of software anomalies in robotic systems, potentially improving operational reliability and reducing downtime.

Ship repair is hazardous, often presenting unsuitable working areas and risks due to the ship’s configuration. Welding tasks are particularly dangerous due to the high temperatures generated, high enough to melt the metal in structural elements, bulkheads, linings, and tanks. This study investigates the consequences of temperature distribution during the welding of naval plates and proposes some accident prevention measures. Industry working conditions were reproduced, including the materials, procedures, and tools used, as well as the certified personnel employed. DH 36-grade naval steel, with a composition of C max. 0.18%, Mn 0.90–1.60%, P 0.035%, S 0.04%, Si 0.10–0.50%, Ni max 0.4%, Cr max 0.25%, Mo 0.08%, Cu max 0.35%, Cb (Nb) 0.05%, and V 0.1%, was welded via FCAW-G (Gas-Shielded Flux-Cored Arc Welding), selected for this study because it is one of the most widely practiced in the naval industry. The main sensor used in the experiments was an FLIR model E50 thermographic camera, and thermal waxes were employed. The results for each thickness case are presented in both graphical and tabular form to provide accurate and actionable guidelines, prioritizing safety. After studying the butt jointing of naval plates of various thicknesses (8, 10, and 15 mm), safe distances to maintain were proposed to avoid risks in the most unfavorable cases: 350 mm from the welding seam to avoid burn injuries to unprotected areas of the body and 250 mm from the welding seam to avoid producing flammable gases. These numbers are less accurate but easier to remember, which prevents errors in the face of hazards throughout a long working day.

The eight papers included in this special issue represent a selection of extended contributions presented at the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2022 held in Salamanca, Spain, September 6th-8th, 2022, and organized by the BISITE group at University of Salamanca. SOCO 2022 international conference represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines which investigate, simulate, and analyse very complex issues and phenomena. This special issue is aimed at practitioners, researchers, and postgraduate students who are engaged in developing and applying advanced intelligent systems principles to solve real-world problems in the mentioned fields.

The six papers included in this special issue represent a selection of extended contributions presented at the 17th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2022 held in Salamanca, Spain, September 6th-8th, 2022, and organized by the BISITE group at the University of Salamanca. The International Conference on Hybrid Artificial Intelligence Systems (HAIS 2022) has become a unique, established, and broad interdisciplinary forum for researchers and practitioners who are involved in developing and applying symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques to present the most relevant achievements in this field. HAIS Series of Conferences provides an interesting opportunity to present and discuss the latest theoretical advances and real-world applications in this multidisciplinary research field.

Accelerated human population growth and global economic development have significantly increased the demand for food, particularly for dairy and meat products, challenging the livestock industry to seek more efficient and sustainable management approaches. Precision Livestock Farming (PLF), focused on individualized cattle monitoring, has positioned itself as a key solution to this challenge. In this sense, activity monitoring collars represent a promising innovation, allowing detailed, real-time observation of the behavior of each animal. In this context, this paper analyzes and compares three dimensional reduction techniques (Kernel PCA, Laplacian Eigenmaps, and UMAP) to characterize and classify the daily behavior of dairy cows in an intensive farm based on information obtained through activity monitoring collars. The results achieved have shown how UMAP stands out as a particularly effective technique as visual tool to individualized or small-group identification of cows with similar patterns. The capacity for characterization and classification is crucial as a preliminary step for developing predictive models focused on detecting anomalous events, such as diseases, calving, or estrus, thus enhancing the efficiency of herd management and contributing to the sector’s sustainability.

As it is well known, mobile phones have become a basic gadget for any individual that usually stores sensitive information. This mainly motivates the increase in the number of attacks aimed at jeopardizing smartphones, being an extreme concern above all on Android OS, which is the most popular platform in the market. Consequently, a strong effort has been devoted for mitigating mentioned incidents in recent years, even though few researchers have addressed the application of visualization techniques for the analysis of malware. Within this field, the present work proposes the extension of a new technique called Hybrid Unsupervised Exploratory Plots to visualize Android malware datasets. More precisely, the novel Beta-Hebbian Learning (BHL) method is applied for the first time and validated under the frame of Hybrid Unsupervised Exploratory Plots, in conjunction with clustering methods. The informative visualization achieved provides a picture of the structure of the malware families, allowing subsequent analysis of their organization. To validate the Hybrid Unsupervised Exploratory Plot extension and its tuning, the popular Android Malware Genome dataset has been used in the experimental setting. Promising results have been obtained, suggesting that BHL applied in combination with clustering techniques in Hybrid Unsupervised Exploratory Plots are a viable resource for the visualization of malware families.

The eight papers included in this special issue represent a selection of extended contributions presented at the 16th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2021 held in Bilbao, Spain, September 22nd-24th, 2021, and organized by the BISITE group and the University of Deusto.The International Conference on Hybrid Artificial Intelligence Systems (HAIS 2021) has become a unique, established, and broad interdisciplinary forum for researchers and practitioners who are involved in developing and applying symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques to present the most relevant achievements in this field.HAIS Series of Conferences provides an interesting opportunity to present and discuss the latest theoretical advances and real-world applications in this multidisciplinary research field.

This research establishes a foundational framework for the development of virtual sensors and provides significant preliminary results. Our study specifically focuses on identifying the key factors essential for accurately predicting total nitrogen in the effluent of wastewater treatment plants. This contribution enhances the predictive capabilities and operational efficiency of these plants, demonstrating the practical benefits of integrating advanced feature selection methods and innovative sensor technologies. These findings provide crucial insights and pave the way for future advancements in the field. In this study, four different feature selection methods are employed to comprehensively explore the variables influencing total nitrogen predictions. The effectiveness of these methods is then evaluated by applying three regression techniques. The findings indicate acceptable levels of accuracy in all applied cases, with one method demonstrating particularly promising results, applicable to several wastewater treatment plants. This validation of the selected variables not only underlines their effectiveness, but also lays the foundation for future virtual sensor applications. The integration of such sensors promises to improve the accuracy and reliability of predictions, marking a significant advance in wastewater treatment plant instrumentation.

The Internet of Things (IoT) is a fast-growing technology that connects everyday devices to the Internet, enabling wireless, low-consumption and low-cost communication and data exchange. IoT has revolutionized the way devices interact with each other and the internet. The more devices become connected, the greater the risk of security breaches. There is currently a need for new approaches to algorithms that can detect malware regardless of the size of the network and that can adapt to dynamic changes in the network. Through the use of a multi-agent reinforcement learning algorithm, this paper proposes a novel algorithm for malware detection in IoT devices. The proposed algorithm is not strongly dependent on the size of the IoT network due to the that its training is adapted using time differences if the IoT network size is small or Monte Carlo otherwise. To validate the proposed algorithm in an environment as close to reality as possible, we proposed a scenario based on a real IoT network, where we tested different malware propagation models. Different simulations varying the number of agents and nodes in the IoT network have been developed. The result of these simulations proves the efficiency and adaptability of the proposed algorithm in detecting malware, regardless of the malware propagation model.

Batteries are a fundamental storage component due to its various applications in mobility, renewable energies and consumer electronics among others. Regardless of the battery typology, one key variable from a user's perspective is the remaining energy in the battery. It is usually presented as the percentage of remaining energy compared to the total energy that can be stored and is labeled State Of Charge (SOC). This work addresses the development of a hybrid model based on a Lithium Iron Phosphate (LiFePO4) power cell, due to its broad implementation. The proposed model calculates the SOC, by means of voltage and electric current as inputs and the latter as the output. Therefore, four models based on k-Means, Agglomerative Clustering, Gaussian Mixture and Spectral Clustering techniques have been tested in order to obtain an optimal solution.

The current global population growth has intensified the demand for feed, especially dairy and meat products. In response to this growing need, precision livestock farming has become essential to manage animals efficiently and sustainably. Within this new approach, individual monitoring of each animal is essential. To this end, activity monitoring collars have been postulated as one of the most valuable tools for this purpose. With this in mind, three dimensional reduction methods (ICA, Isomap and t-SNE) have been analyzed in this study to identify patterns in the daily behavior of dairy cows through data collected by these collars. T-SNE was found to be especially effective in distinguishing individual or small group behaviors of animals, which is crucial for improving herd management and early detection of health or behavioral problems.

The present research describes a novel adaptive anomaly detection method to optimize the performance of nonlinear and time-varying systems. The proposal integrates a centroid-based approach with the real-time identification technique Recursive Least Squares. In order to find anomalies, the approach compares the present system dynamics with the average (centroid) of the dynamics found in earlier states for a given setpoint. The system labels the dynamics difference as an anomaly if it rises over a determinate threshold. To validate the proposal, two different datasets obtained from a level control plant operation have been used, to which anomalies have been artificially added. The results shown have determined a satisfactory performance of the method, especially in those processes with low noise.

During the course of the last decade, the concern about climate change effects has increased significantly, being one of the key issues in scientific, political, and economic fields. This situation is the consequence of fossil fuel dependency, with the related greenhouse emissions that contribute to speeding up global warming. In this context, governments have been developing different roadmaps to tackle this emergency. The pollution of countries and companies has been restricted by setting gasses emission rights, which, combined with economic aid to promote green energies, have resulted in increased renewable energy systems. Although the scene in terms of emissions tends to improve, several problems are derived from this sudden change in the energy market, where technologies such as thermal power plants are being closed. The main issue lies in the fact that energy bill has become more expensive due to the intermittency in the availability of renewable energy sources; furthermore, this circumstance was drastically affected when the war broke out in East Europe in February 2022, leading to historical energy price peaks. In any case, energy companies set different prices throughout the day, having these values have a remarkable fluctuation, with great variation between night and day hours. In light of this scenario, promoting clean, renewable energy generation systems has become about environmental care and economic efficiency; however, the intermittency of renewable resources availability implies that the energy demand must be supplemented by the power network. In this context, a proper forecast of the energy generated by a renewable system plays a key role in an energy management system. Then, for example, when it is expected to generate enough energy to cover the demand and the energy is expensive, the power network is disconnected. At low energy prices, the energy generated can be stored, making the system more efficient. These two examples are used to emphasize the importance of a forecasting model combined with an energy management system. This chapter aims to deal with the most innovative and accurate renewable energy forecasting systems. According to the literature, there is a wide variety of possibilities to face the prediction of energy generation prediction problems depending on the technology to be modeled (wind, geothermal, solar, etc.), the way that the data is considered (time series, isolated points, seasonal data), or the window size to forecast (from minutes to days); furthermore, the use of this kind of predictive system is a key step in implementing digital twins. This cutting-edge technology is especially useful for predictive and corrective maintenance, ensuring the proper system operation, with the consequent energy efficiency benefits.

The seven papers included in this special issue represent a selection of extended contributions presented at the 16th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2021, held in Bilbao, Spain, September 22nd-24th, 2021, and organized by the BISITE group and the University of Deusto.The SOCO 2021 international conference represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines that investigate, simulate, and analyse very complex issues and phenomena.This special issue is aimed at practitioners, researchers, and postgraduate students who are engaged in developing and applying advanced intelligent systems principles to solve real-world problems in the mentioned fields.

The use of renewable energy is expanding globally, driven by the need to reduce greenhouse gas emissions and mitigate climate change. This study focuses on modelling the electrical power generated by photovoltaic panels in a bioclimatic home, analyzing the performance of linear regression and multilayer perceptron models, while considering atmospheric factors such as solar radiation and ambient temperature. The process includes a correlation analysis to select the most relevant variables, followed by dataset preprocessing techniques. Finally, performance metrics of the models are evaluated, which indicate a strong correlation between solar radiation and the power generated, resulting in robust regression models.

Growing dependence on fossil fuels is one of the critical factors accelerating climate change, a global concern that can destabilize ecosystems and economies worldwide. In this context, renewable energy is emerging as a sustainable and environmentally responsible alternative. Among the options, geothermal energy stands out for its ability to provide heat and electricity consistently and efficiently, offering a feasible solution to reduce the carbon footprint and promote more sustainable development in a globalized economy. In this work, a machine learning approach is proposed to predict the behavior of a horizontal heat exchanger from a bioclimatic house. First, a correlation analysis was conducted for optimal feature selection. Then, several regression techniques were applied to predict the output temperature of the geothermal exchanger. Satisfactory prediction results were obtained in different scenarios over the whole dataset. Also, a significant correlation between several sensors was concluded.

LOW-COST HARDWARE PLATFORM IMPLEMENTATION FOR SYSTEM IDENTIFICATION AND EMULATION OF A REAL-LEVEL CONTROL PLANT