Amedeo Buonanno

ENEA | ENEA · Department of Energy Technologies and Renewable Sources

In radiation oncology, the need for a modern Normal Tissue Complication Probability (NTCP) philosophy to include voxel-based evidence on organ radio-sensitivity (RS) has been acknowledged. Here a new formalism (Probabilistic Atlas for Complication Estimation, PACE) to predict radiation-induced morbidity (RIM) is presented. The adopted strategy basi...

Word representation is fundamental in NLP tasks, because it is precisely from the coding of semantic closeness between words that it is possible to think of teaching a machine to understand text. Despite the spread of word embedding concepts, still few are the achievements in linguistic contexts other than English. In this work, analysing the seman...

In this work, we investigate an Information Fusion architecture based on a Factor Graph in Reduced Normal Form. This paradigm permits to describe the fusion in a completely probabilistic framework and the information related to the different features are represented as messages that flow in a probabilistic network. In this way we build a sort of co...

The use of estimation techniques on stochastic models to solve control problems is an emerging paradigm that falls under the rubric of Active Inference (AI) and Control as Inference (CAI). In this work, we use probability propagation on factor graphs to show that various algorithms proposed in the literature can be seen as specific composition rule...

In Smart Energy Grids, the information flow used to make decisions is the result of fusion of different sources. Communication latency, possible sensor faults and inaccuracies, may negatively impact the data quality and hence the taken decisions. For these reasons, the construction of a robust representation of the input signals that replaces and/o...

The eNeuron project aims to develop a pioneering toolbox framework for the optimal design and operation of multi-carrier energy systems, such as integrated local energy communities based on the energy hub concept. In today's rapidly evolving business and regulatory landscape, the evaluation of business models has become a critical endeavor for stak...

The combination of different energy vectors like electrical energy, hydrogen, methane, and water is a crucial aspect to deal with in integrated local energy communities (ILECs). The ILEC stands for a set of active energy users that maximise benefits and minimise costs using optimisation procedures in producing and sharing energy. In particular, the...

Contrary to conventional massive MIMO cellular configurations plagued by inter-cell interference, cell-free massive MIMO systems distribute network resources across the coverage area, enabling users to connect with multiple access points (APs) and boosting both system capacity and fairness across user. In such systems, one critical functionality is...

The decarbonization of the electricity grid is one of the actions that can help reduce fossil fuel emissions, and thus their impact on global warming in the future. This decarbonization will be achieved mainly through the integration and widespread diffusion of renewable power sources. This is also going to be supported by the shift from the paradi...

In response to the pressing need for sustainable energy solutions amidst escalating climate change challenges, the integration of polygenerative hybrid systems within smart microgrids has emerged as a promising avenue. This article delves into the modeling intricacies of such systems, focusing on the ENEA Research Center in Portici, Italy. Employin...

The decarbonization of the electricity grid as one among the actions to reduce fossil fuel emissions, and thus their impact on global warming in the future, will be achieved mainly via the integration and widespread diffusion of renewable power sources. This is going to be supported also by the shift from the paradigm production-transmission-distri...

Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. The...

This paper addresses the problem of joint multiplexing of enhanced Mobile Broadband (eMBB) and massive Machine-Type Communications (mMTC) traffic in the same uplink time-frequency RG. Given the challenge posed by a potentially large number of users, it is essential to focus on a multiple access strategy that leverages artificial intelligence to...

p>By providing important benefits such as reducing primary energy consumption, improving environmental conditions, and increasing resilience of energy supply, integrated local energy communities (ILECs) represent a viable option to centralized energy systems for fostering decarbonization, thanks to the exploitation of synergies coming from differen...

p>By providing important benefits such as reducing primary energy consumption, improving environmental conditions, and increasing resilience of energy supply, integrated local energy communities (ILECs) represent a viable option to centralized energy systems for fostering decarbonization, thanks to the exploitation of synergies coming from differen...

Specification of an appropriate reward function is vital to Reinforcement Learning (RL). This paper proposes a technique to learn the reward function from observed tracklets to clone the behavior of pedestrians moving in an urban area. The information is then incorporated in a Markov Decision Process (MDP)-driven agent represented using Factor Grap...

Human Activity Recognition (HAR) has been a theme of great interest in research, especially thanks to the possible practical applications in the fields of video surveillance, human-machine interaction, gaming, autonomous driving, and health care. Despite this, the problem still remains a very complex challenge and whose definitive solution is still...

By providing important benefits such as reducing primary energy consumption, improving environmental conditions, and increasing resilience of energy supply, integrated local energy communities (ILECs) represent a viable option to centralized energy systems for fostering decarbonization, thanks to the exploitation of synergies coming from different...

Machine learning is becoming a fundamental tool in current energy systems. It helps to obtain accurate predictions of the variable renewable energy (VRE) generation, energy demand, or possible network outages, conferring to power system operators the possibility to make the needed actions to balance load and generation in intraday and day-ahead sch...

p>This paper presents a review study of energy communities (ECs) in Europe, and discusses the future development of such communities in Europe – both related to energy technologies, energy carriers, regional conditions (North, Central and South of Europe), emerging regulatory development etc. From the analysis, it emerged that the future ECs in Eur...

p>This paper presents a review study of energy communities (ECs) in Europe, and discusses the future development of such communities in Europe – both related to energy technologies, energy carriers, regional conditions (North, Central and South of Europe), emerging regulatory development etc. From the analysis, it emerged that the future ECs in Eur...

p>The main goal of eNeuron H2020 project (Nov 2020-Oct. 2024, ID: 957779) is to develop innovative tools for the optimal design and operation of local energy communities, integrating distributed energy resources and multiple energy carriers at different scales. This paper presents a review study of the enabling conditions for the deployment of inte...

p>The main goal of eNeuron H2020 project (Nov 2020-Oct. 2024, ID: 957779) is to develop innovative tools for the optimal design and operation of local energy communities, integrating distributed energy resources and multiple energy carriers at different scales. This paper presents a review study of the enabling conditions for the deployment of inte...

Climate change is increasing the occurrence of the so-called heatwaves with a trend that is expected to worsen in the next years due to global warming. The growing intensity and duration of these extreme weather events are leading to a significant number of power system failures, especially in urban areas. This is drastically affecting the reliabil...

Expert team decision-making demonstrates that effective teams have shared goals, shared mental models to coordinate with minimal communication, establish trust through cross-training, and match task structures through planning. The key questions: Do best practices of human teams translate to hybrid human-AI agent teams, or autonomous agents alone?...

Despite their positive effects on the decarbonization of energy systems, renewable energy sources can dramatically influence the short-term scheduling of distributed energy resources (DER) in smart grids due to their intermittent and non-programmable nature. Renewables’ uncertainties need to be properly considered in order to avoid DER operation st...

Aiming at integrating different energy sectors and exploiting the synergies coming from the interaction of different energy carriers, sector coupling allows for a greater flexibility of the energy system, by increasing renewables' penetration and reducing carbon emissions. At the local level, sector coupling fits well in the concept of an integrate...

The main goal of eNeuron H2020 project (Nov 2020-Oct. 2024, ID: 957779) is to develop innovative tools for the optimal design and operation of local energy communities, integrating distributed energy resources and multiple energy carriers at different scales. This paper presents a review study of the enabling conditions for the deployment of integr...

We present a unified approach to multi-agent autonomous coordination in complex and uncertain environments, using path planning as a problem context. We start by posing the problem on a probabilistic factor graph, showing how various path planning algorithms can be translated into specific message composition rules. This unified approach provides a...

Electrical load forecasting has a fundamental role in the decision-making process of energy system operators. When many users are connected to the grid, high-performance forecasting models are required, posing several problems associated with the availability of historical energy consumption data for each end-user and training, deploying and mainta...

This deliverable is the third one in the series of three reports that have been developed in the work package (WP) 2 "Limitations and shortcomings for optimal use of local resources" from the H2020 project eNeuron. The main objective of this deliverable is to identify the main barriers, technical limitations, shortcomings and obstacles which can li...

Due to the recent fast growth of Distributed Energy Resources (DER) and conversion technologies, numerous planning and evaluation models and approaches are available in the literature to enhance local integration of DER under the energy hub concept. However, most of the works address the design problem by using a limited number of aspects to achiev...

This deliverable is prepared within WP3 and the Task 3.1 "Identification of the "Local Integrated Energy Community" subject through the assessment of the current regulatory framework in Europe" in the eNeuron project. The aim of this WP is to identify the “Local Integrated Energy Community” subject based on the most recent regulatory developments a...

Despite the large diffusion and use of embedding generated through Word2Vec, there are still many open questions about the reasons for its results and about its real capabilities. In particular, to our knowledge, no author seems to have analysed in detail how learning may be affected by the various choices of hyperparameters. In this work, we try t...

This deliverable describes the status of the eNeuron project at the end of the first project year, as well as the activities planned for the second project year. During the first project year, most of scientific and dissemination activities started, with the deliverables and milestones for this period having been accomplished. This progress has bee...

This deliverable is the second one in the series of three reports that are planned to be developed in the activity "Limitations and shortcomings for optimal use of local resources" in the H2020 project eNeuron. After the completion of a previous deliverable of the eNeuron project regarding the critical analysis of the European policy and regulatory...

Even if path planning can be solved using standard techniques from dynamic programming and control, the problem can also be approached using probabilistic inference. The algorithms that emerge using the latter framework bear some appealing characteristics that qualify the probabilistic approach as a powerful alternative to the more traditional cont...

This deliverable includes the first results of work package 2 "Limitations and shortcomings for optimal use of local resources" of the eNeuron project and is the first in a series of three reports that is looking into regulatory aspects concerning the methodologies to be developed within the project. The following sub-activities will identify the p...

Bayesian networks in their Factor Graph Reduced Normal Form are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this wor...

Probability models are emerging as a promising framework to account for “intelligent” behavior. In this article, probability propagation is discussed to model agent's motion in potentially complex grids that include goals and obstacles. Tensor messages in the state-action space (due to grid structure, states are 2-D and the concomitant probability...

Question-answering (QA) is certainly the best known and probably also one of the most complex problem within Natural Language Processing (NLP) and artificial intelligence (AI). Since the complete solution to the problem of finding a generic answer still seems far away, the wisest thing to do is to break down the problem by solving single simpler pa...

Word representation is fundamental in NLP tasks, because it is precisely from the coding of semantic closeness between words that it is possible to think of teaching a machine to understand text. Despite the spread of word embedding concepts, still few are the achievements in linguistic contexts other than English. In this work, analysing the seman...

Probability models have been proposed in the literature to account for "intelligent" behavior in many contexts. In this paper, probability propagation is applied to model agent's motion in potentially complex scenarios that include goals and obstacles. The backward flow provides precious background information to the agent's behavior, viz., inferen...

Question-answering (QA) is certainly the best known and probably also one of the most complex problem within Natural Language Processing (NLP) and artificial intelligence (AI). Since the complete solution to the problem of finding a generic answer still seems far away, the wisest thing to do is to break down the problem by solving single simpler pa...

We report a number of experiments on a deep convolutional network in order to gain a better understanding of the transformations that emerge from learning at the various layers. We analyze the backward flow and the reconstructed images, using an adaptive masking approach in which pooling and nonlinearities at the various layers are represented by d...

Bayesian networks in their Factor Graph Reduced Normal Form (FGrn) are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable, even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In...

Contextual information can be used to help object detection in video and images, or to categorize text. In this work we demonstrate how the Latent Variable Model, expressed as a Factor Graph in Reduced Normal Form, can manage contextual information to support a scene understanding task. In an unsupervised scenario our model learns how various objec...

Bayesian networks in their Factor Graph Reduced Normal Form (FGrn) represent a very appealing paradigm for the realization of structures for probabilistic inference. Unfortunately, the computational and memory complexity of such networks remains high, especially if the network has to extend to large structures such as multi-layers and highly connec...

In this work a Bayesian Multi-Layer Network, using the Factor Graphs in Reduced Normal Form (FGrn) paradigm, has been applied to a two-dimensional lattice. Several Latent Variable Models (LVMs) are arranged in a quadtree hierarchy built on top of a bottom layer of random variables that represent a collection of spatially distributed discrete varia...

Bayesian clustering implemented on a small Factor Graph is utilized in this work to perform associative recall and pattern recognition on images. The network is trained using a maximum likelihood algorithm on images from a standard data set. The two-class labels are fused with the image data into a unique hidden variable. Performances are evaluated...

We apply belief propagation to a Bayesian bipartite graph composed of discrete independent hidden variables and discrete visible variables. The network is the Discrete counterpart of Independent Component Analysis (DICA) and it is manipulated in a factor graph form for inference and learning. A full set of simulations is reported for character imag...

We build a multi-layer architecture using the Bayesian framework of the Factor Graphs in Reduced Normal Form (FGrn). This model allows great modularity and unique localized learning equations. The multi-layer architecture implements a hierarchical data representation that via belief propagation can be used for learning and inference in pattern comp...

We apply belief propagation to a Bayesian bipartite graph composed of discrete independent hidden variables and discrete visible variables. The network is the Discrete counterpart of Independent Component Analysis (DICA) and it is manipulated in a factor graph form for inference and learning. A full set of simulations is reported for character imag...

Bayesian clustering implemented on a small Factor Graph is utilised in this work to perform associative recall and pattern recognition on images. The network is trained using a maximum likelihood algorithm on images from a standard data set. The two-class labels are fused with the image data into a unique hidden variable. Performances are evaluated...