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Two-stage dynamic management in energy communities using a decision system based on elastic net regularization
A two-stage forecasting-optimization system for efficient management of energy communities.
A. Rosato, M. Panella, A. Andreotti, Osama A. Mohammed, R. Araneo,
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Two-stage dynamic management in energy communities using a decision system based on elastic net regularization

A. Rosato, M. Panella, A. Andreotti, Osama A. Mohammed, R. Araneo,
The future of distributed energy: how AI is revolutionizing energy communities
Energy communities are undergoing a groundbreaking transformation. With the increasing adoption of renewable energy sources and energy storage systems, integrating advanced technologies is essential to address challenges such as managing production variability and balancing consumption. Here, artificial intelligence proves indispensable, offering innovative solutions to enhance efficiency and sustainability.
Predicting the future: LSTM networks to anticipate energy demand and supply
Recurrent neural networks, such as LSTMs, enable precise forecasting of renewable energy production and load consumption. This approach anticipates seasonal and daily variations, improving the operational management of distributed resources. A practical example: in an energy community in Southern Italy, which includes 11 loads and 3 distributed generators such as wind turbines and photovoltaic systems, consumption and production profiles were predicted with an average error margin of less than 10%. Simulations included complex scenarios, such as variations in photovoltaic generation during winter, demonstrating that even under adverse weather conditions, the model quickly adapts while maintaining reliable forecasts.
Elastic Net: dynamic optimization to balance consumption and generation
The integration of the Elastic Net methodology in energy management has introduced unprecedented efficiency levels. Using regularization parameters that balance accuracy and efficiency, the system optimizes the operation of storage systems, generators, and loads. In tests conducted in June and December, cumulative imbalances were reduced from 11.5 MWh to 5.5 MWh and from 12.1 MWh to 10.3 MWh, respectively. This result was achieved through careful load management without compromising user comfort. Additionally, batteries were strategically deployed to minimize sudden variations in the grid. The optimization system limits excessive battery use, reducing the risk of premature wear and ensuring optimal operational lifespan.
Real-world applications: success stories in energy communities
The results are tangible: in tests conducted on real data, the system significantly reduced grid imbalances by using storage systems strategically and minimizing reliance on unsustainable energy sources. Moreover, thanks to short-term forecasting, resources such as wind turbines, photovoltaic systems, and electric vehicle charging stations were effectively integrated. This allowed users to actively participate in energy management through economic incentives and reduced operational costs.
A model for sustainable and shared energy
This technology not only enhances the resilience of local grids but also serves as a replicable paradigm in both urban and rural contexts. The combination of AI, Elastic Net, and LSTM networks offers a scalable model for the future of smart grids, where sustainability and innovation go hand in hand.
Multivariate Time Series Analysis for Electrical Power Theft Detection in the Distribution Grid
A convolutional neural network analyzes multivariate time series to detect energy theft in distribution grids effectively.
A. Ceschini, A. Rosato, F. Succetti, R. Araneo, M. Panella
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Multivariate Time Series Analysis for Electrical Power Theft Detection in the Distribution Grid

A. Ceschini, A. Rosato, F. Succetti, R. Araneo, M. Panella
How artificial intelligence transforms the fight against energy theft
The challenge of energy theft is one of the most critical issues for distribution network operators. In addition to causing significant economic losses, theft can compromise the quality of electricity supply, lead to blackouts, and slow down the transition to sustainable energy. With the introduction of smart grids and advanced meters, it is now possible to tackle this issue using cutting-edge artificial intelligence technologies. This innovative study proposes a system based on deep neural networks, such as CNNs, to detect energy theft through the analysis of multivariate time series, demonstrating how AI can revolutionize the automatic detection of anomalies.
A solution based on real-world data
The developed system leverages CNNs to analyze data collected in real-world scenarios, such as industrial sites with verified theft incidents. The data, acquired from multiple sensors, include variables such as cumulative active and reactive energy, total monthly power, and average power calculated across different time slots. These datasets, manually labeled based on certified reports, enable the model to identify anomalies in consumption and pinpoint the moments when theft occurs. Thanks to its multivariate structure, the system can uncover hidden correlations among variables, providing a more comprehensive view compared to traditional methods.
Promising performance in real-world conditions
The system was tested on five years of data, encompassing consumption profiles of users with 548 sequences sampled monthly. With an average accuracy of 76.1% in binary classification and 78.4% in a multiclass problem, the model has proven to be reliable and robust. Despite the complexity of the analyzed sequences and the variability of the case studies, the CNN effectively distinguished between regular and irregular consumption, significantly reducing error margins.
A future without waste
This technology is not limited to detecting theft; it marks a significant step toward more efficient and resilient management of distribution networks. By automating traditionally slow and costly processes, such as manual meter inspections, the system helps reduce economic and energy losses. Additionally, the scalability of the model makes it suitable for diverse contexts, from urban to industrial areas, seamlessly integrating with modern smart grid infrastructures.
Challenges and perspectives of smart grid systems in islands: a real case study
Integrating renewables with AI tools offers sustainable solutions, especially in isolated contexts.
F. Succetti, A. Rosato, R. Araneo, G. Di Lorenzo, M. Panella
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Challenges and perspectives of smart grid systems in islands: a real case study

F. Succetti, A. Rosato, R. Araneo, G. Di Lorenzo, M. Panella
Renewable Energy Challenges for Island Systems
Islands like Ponza face unique challenges in achieving energy sustainability, primarily due to their reliance on imported diesel fuel. This dependency leads to high costs and frequent instability. Transitioning to renewable energy sources (RESs), such as photovoltaic (PV) and wind power, offers a promising alternative. However, intermittent energy production, limited storage options, and environmental constraints complicate the integration of RESs. Advanced technologies, including machine learning and deep learning, are instrumental in predicting energy demand and optimizing grid management.
The Role of Storage and Grid Innovations
Battery Energy Storage Systems (BESSs) play a pivotal role in stabilizing power grids with high RES penetration. For Ponza, the planned BESS infrastructure will provide a spinning reserve, manage fluctuations in RES production, and ensure consistent electricity supply during peak tourist seasons. Modernizing the grid further involves integrating automation, enabling real-time adjustments to power flows, and facilitating the seamless incorporation of new RES installations.
Predictive Insights Through AI
Artificial intelligence and deep learning techniques are applied to forecast energy production and demand accurately. These predictions are essential for designing efficient energy storage solutions and ensuring optimal use of RESs. On Ponza, machine learning models estimate future energy demand, incorporating variables like tourist influx, seasonal trends, and new electricity-driven initiatives such as electric vehicle charging stations and water desalination units.
Pathways to Sustainability
Achieving the 2030 targets outlined for Ponza involves significant upgrades to energy infrastructure. These include deploying RES systems with a capacity of up to 2.16 MW, installing advanced BESSs, and modernizing grid operations. By addressing environmental and logistical constraints, this approach aligns energy production with demand, reduces diesel reliance, and fosters long-term sustainability. The integration of smart grid systems ensures adaptability and efficiency, setting a benchmark for other islands globally.
An adaptive embedding procedure for time series forecasting with deep neural networks
A novel deep learning model that integrates adaptive embedding with bidirectional LSTMs to enhance time series forecasting.
F. Succetti, A. Rosato, M. Panella
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An adaptive embedding procedure for time series forecasting with deep neural networks

F. Succetti, A. Rosato, M. Panella
More Reliable Forecasting with AI
From finance to energy and meteorology, time series forecasting is a crucial challenge across various industries. Analyzing historical data to predict future trends is often hindered by non-linearity, high variability, and long-term dependencies. Deep neural networks have proven to be powerful tools for tackling these issues, but their effectiveness is often limited by inefficient data handling and a heavy reliance on parameter optimization. A new approach based on adaptive embedding is revolutionizing time series forecasting by enhancing both accuracy and flexibility. This technique automatically extracts a compressed representation of historical data, reducing problem complexity and optimizing the predictive capabilities of neural networks.
LSTM and Adaptive Embedding: How It Works
At the core of this innovation lies a bidirectional Long Short-Term Memory (LSTM) network, structured in two layers. The first layer performs adaptive embedding, identifying the most relevant patterns in the time series without human intervention. This pre-training phase enables the model to better understand the underlying data structure. The second layer then uses this information to make more precise predictions. The key idea is to eliminate the need for separate feature extraction algorithms, integrating data analysis directly within the neural network. This not only simplifies the process but also makes the system more efficient and applicable to any context, from financial market fluctuations to intelligent energy management.
Real-World Applications
This approach has been tested in real-world scenarios, demonstrating high accuracy and versatility. It has been successfully applied to forecasting energy consumption, photovoltaic production, and financial data. The results show that the model reduces forecasting errors compared to traditional techniques, significantly improving its ability to adapt to highly dynamic data. A crucial advantage is its generalization capability: the system can be used across different industries without requiring extensive customization. This makes it a powerful tool for businesses and institutions that need reliable forecasts to optimize resource management.
Towards More Efficient Predictive Intelligence
The integration of adaptive embedding and deep neural networks marks a breakthrough in the world of forecasting. This approach not only improves model accuracy but also reduces computational load, making it ideal for real-time applications. With this technology, AI is becoming an increasingly strategic tool for understanding and anticipating complex phenomena, helping businesses and researchers make more informed, data-driven decisions.
08/08/2024
A Neural Network Symbolic Approach to Structural Health Monitoring in Aerospace Applications
A symbolic deep learning approach enhances structural health monitoring in aerospace achieving near-perfect damage classification.
F. Angeletti, F. Succetti, M. Panella, A. Rosato
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08/08/2024

A Neural Network Symbolic Approach to Structural Health Monitoring in Aerospace Applications

F. Angeletti, F. Succetti, M. Panella, A. Rosato
Structural Monitoring: A Challenge for Space Missions
Ensuring the structural integrity of spacecraft and satellites is one of the most critical challenges for aerospace missions. Traditional visual inspection methods are often impractical in orbit, making it necessary to adopt advanced solutions based on distributed sensors and automated analysis algorithms. Among the main threats to the stability of space structures are impacts with orbital debris, extreme thermal oscillations, and material fatigue, all of which can compromise the proper functioning of satellites and their appendages, such as solar panels.
AI and Neural Networks for Damage Detection
The integration of Artificial Intelligence into structural health monitoring represents a paradigm shift. The use of deep neural networks, particularly Long Short-Term Memory (LSTM) networks, enables the analysis of time-series data from accelerometers and other sensors, identifying anomalies that could indicate structural damage. The adopted model leverages an innovative approach, combining symbolic representation of time series with a recurrent neural network architecture. By compressing information through Symbolic Aggregate approXimation (SAX), data dimensionality is reduced, improving processing speed and enhancing the model’s ability to recognize recurring patterns. This method transforms complex time-series data into symbolic sequences, simplifying the classification process and making the algorithm more robust to data variations.
Applications in Space: From Simulation to Reality
To test the system’s effectiveness, a simulation of a satellite with flexible solar panels equipped with acceleration sensors was conducted. The model was evaluated under multiple damage scenarios, replicating realistic conditions of impacts with space debris. The results showed that using symbolic representation enhances the accuracy of damage classification, achieving an almost 100% precision and significantly reducing false positives and negatives. The ability of an AI model to autonomously and reliably detect structural damage without human intervention is a crucial step forward for space missions. The combination of recurrent neural networks and dimensionality reduction techniques opens new possibilities for onboard automatic monitoring, essential for ensuring the safety and longevity of space infrastructures.
Towards a Future of Intelligent Monitoring
The adoption of AI in the aerospace sector is transforming the way space systems are monitored and managed. The ability to implement autonomous solutions based on deep learning and symbolic representation reduces computational load while improving diagnostic reliability. With advancements in onboard computing technologies, these solutions could become standard for satellite and space module monitoring, paving the way for a new era of autonomous and intelligent space exploration.
A variational approach to quantum gated recurrent units
A faster and efficient Quantum Gated Recurrent Unit (QGRU) improves time series forecasting.
A. Ceschini, A. Rosato, M. Panella
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A variational approach to quantum gated recurrent units

A. Ceschini, A. Rosato, M. Panella
Quantum Artificial Intelligence Revolutionizing Predictions
From finance to renewable energy, time series forecasting is a cornerstone for optimizing strategic decisions and improving resource management. However, traditional deep learning models, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, face significant limitations: high computational costs, long training times, and challenges in handling long-term dependencies. The integration of Quantum Artificial Intelligence (QAI) with RNNs opens new possibilities, leveraging quantum superposition and entanglement to enhance computational efficiency and forecasting accuracy. An innovative architecture based on Quantum Gated Recurrent Units (QGRU) introduces a faster and more efficient model compared to both classical and existing quantum alternatives.
Quantum GRU: A Faster and More Efficient Model
The QGRU architecture is based on Variational Quantum Circuits (VQC), which process temporal data in a high-dimensional space, maximizing the potential of current quantum devices. The model combines parametric quantum layers with two classical preprocessing and postprocessing layers, optimizing data input and output. One of the main advantages of this architecture is the 25% reduction in quantum parameters compared to Quantum LSTM (QLSTM) networks. This translates into greater computational efficiency: the QGRU model is about 25% faster in both training and inference compared to QLSTM, making it more suitable for implementation on real quantum hardware and simulators.
Real-World Applications: From Meteorology to Energy
The effectiveness of QGRU has been tested across various real-world scenarios, demonstrating its superiority over both classical models and other quantum solutions. One of the most challenging applications is the prediction of solar cycles, where sunspots exhibit high variability and make forecasting particularly complex. The quantum model has shown a remarkable ability to adapt to these fluctuations, outperforming classical neural networks in handling noisy and nonlinear data. In the field of renewable energy, QGRU has been applied to wind power generation, an area where rapid and unpredictable variations pose significant challenges for grid management. The model has proven capable of producing more stable and reliable predictions, reducing the average forecasting error by 40% compared to conventional LSTM networks. This improved accuracy is crucial for optimizing energy distribution and integrating renewables more efficiently into the electrical grid. QGRU has also been tested on periodic time series, demonstrating superior stability and modeling capabilities. Unlike traditional approaches, which struggle with long-term dependencies and complex temporal patterns, the quantum model effectively captures underlying trends, offering a more robust and adaptable solution for time series forecasting in multiple domains.
Towards a Quantum Future for Deep Learning
The implementation of quantum recurrent neural networks marks a significant step forward in time series forecasting, offering a winning combination of accuracy and computational speed. As quantum hardware continues to evolve, these architectures could become increasingly accessible, paving the way for new applications in strategic sectors such as finance, healthcare, and energy. QAI is transforming how we interpret data and make decisions, ushering machine learning into a new era. The future of forecasting? Faster, more precise, and… quantum-powered.
14/11/2024
An explainable fast deep neural network for emotion recognition
A fast, explainable deep neural network enhances emotion recognition by optimizing facial landmark analysis.
F. Di Luzio, A. Rosato, M. Panella
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14/11/2024

An explainable fast deep neural network for emotion recognition

F. Di Luzio, A. Rosato, M. Panella
Facial Expressions: The Key to Non-Verbal Communication
Facial expressions are the foundation of human non-verbal communication. A smile, a look of surprise, an expression of disgust—each emotion is conveyed through subtle facial micro-movements that, until recently, were difficult for machines to interpret with precision. Today, thanks to an innovative deep neural network, not only can emotions be recognized with high accuracy, but this can now be done quickly and, most importantly, in an explainable manner.
From Opaque Intelligence to Understandable Intelligence
Deep neural networks have always faced a major challenge: their "black box" nature. While they make highly accurate predictions, their decision-making processes have long remained obscure. This new model overcomes this barrier through the use of explainable AI, which can identify and prioritize the most relevant features for emotion recognition.By integrating Integrated Gradients, an advanced Explainable AI technique, the model can analyze the contribution of each facial reference point (landmark) in classifying emotions. The original input consists of 468 facial landmarks extracted from video sequences, but through data relevance analysis, the system can reduce the number of utilized points without compromising accuracy. Tests have shown that by reducing the landmarks to 128, the model maintains an accuracy above 97% for certain emotions, such as surprise and happiness, while significantly lowering computational costs.
Computational Efficiency and Performance
Optimizing the number of features is not just a theoretical exercise; it leads to tangible improvements in model performance. Tests conducted on standard datasets such as CK+ have revealed a significant reduction in inference time, making the system suitable for real-time applications.
Real-World Applications: From Healthcare to Security
The applications of this technology go far beyond basic facial recognition. In the medical field, a more precise analysis of facial expressions can support neurological and psychiatric diagnoses by detecting early signs of emotional disorders. In security systems, real-time monitoring of facial expressions can help identify suspicious intent, contributing to safer public spaces.The optimization of AI models through explainability techniques represents a major leap forward: not only does it ensure more reliable and transparent functioning, but it also paves the way for a future where AI is not just intelligent but also comprehensible and accessible.
05/12/2024
Enhancing Autism Detection Through Gaze Analysis Using Eye Tracking Sensors and Data Attribution with Distillation in Deep Neural Networks
A deep learning model enhances early autism diagnosis by analyzing visual patterns with eye tracking.
F. Colonnese, F. Di Luzio, A. Rosato, M. Panella
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05/12/2024

Enhancing Autism Detection Through Gaze Analysis Using Eye Tracking Sensors and Data Attribution with Distillation in Deep Neural Networks

F. Colonnese, F. Di Luzio, A. Rosato, M. Panella
Beyond Traditional Diagnosis: The Role of AI in Autism Detection
Autism Spectrum Disorder (ASD) is typically diagnosed through behavioral assessments, structured questionnaires, and clinician observations. While effective, these methods rely heavily on subjective interpretation, which can lead to variability in diagnostic outcomes. The integration of artificial intelligence into medical diagnostics opens up new possibilities for more precise and objective screening, especially when combined with advanced technologies such as eye tracking.
Decoding Gaze Patterns with AI
Individuals with ASD often exhibit unique gaze behaviors, such as reduced eye contact or a preference for focusing on peripheral objects rather than social stimuli like faces. Eye tracking sensors provide a powerful tool to quantify these behaviors, capturing precise information about how a person scans and fixates on different elements in a visual scene. By leveraging deep neural networks, these gaze patterns can be analyzed in real-time, identifying characteristics that distinguish individuals with ASD from neurotypical individuals.
Optimizing Accuracy Through Data Selection
Processing large datasets for AI training can be computationally expensive and time-intensive. To enhance efficiency, an innovative approach known as data attribution is used, allowing AI models to prioritize the most relevant training samples while filtering out noisy or misleading data. By applying a technique called TracIn, researchers can evaluate how each data point influences the model’s learning process, refining the dataset without compromising accuracy. In fact, results show that even when trained on just 77% of the dataset, the model maintained a classification accuracy of 94.35%, surpassing benchmarks and proving that selecting high-quality data is more effective than simply increasing the dataset size.
From Lab to Real-World Applications
This technology has the potential to transform autism screening and diagnosis. AI-powered gaze analysis could be implemented in clinical settings, providing clinicians with an additional, objective tool to support early detection. It could also be integrated into portable diagnostic devices, making autism screening more accessible in schools or pediatric clinics. Moreover, by identifying the most influential gaze patterns linked to ASD, this research enhances the broader understanding of visual attention differences, contributing to improved therapeutic approaches.
A Future of AI-Assisted Diagnosis
The combination of AI, deep learning, and eye tracking represents a major step toward more reliable and interpretable medical AI applications. By improving accuracy while reducing computational overhead, this approach not only refines ASD classification but also lays the groundwork for integrating AI-driven insights into clinical practice. In the near future, these models could be adapted for other neurodevelopmental conditions, further bridging the gap between AI innovation and healthcare.

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