PEOPLEDaniele Antonucci

de en it

Daniele Antonucci

Senior Researcher
Institute for Renewable Energy

T +39 0471 055 659

daniele.antonucci@eurac.edu

https://orcid.org/0000-0002-4736-0711

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About

About:

Data Analysis
Building monitoring
IoT
Machine Learning

Daniele Antonucci, graduated in Building Systems Engineering at Politecnico di Milano and PhD in Sustainable Energy and Technologies at the University of Bolzano, is senior researcher at the Institute for Renewable Energy of Eurac Research in Bolzano.
He works mainly in the definition of building monitoring systems, data analysis, analytical modeling and application development related to energy efficiency of buildings.
Since 2012 he follows European projects related to the use of data and big data for buildings. He is an IPMVP certifier

LinkedIn: https://www.linkedin.com/in/daniele-antonucci-311290b5/
ResarchGate: https://www.researchgate.net/profile/Daniele-Antonucci

Scientific Publications

Conference proceedings article2025

BREEZE Project: Advancing PV Implementation in Building Renovations

Celik D, Stierstorfer J, Morin C, Garabetian T, Pozza C, Babich F, Gupta A, Raul Morantes Quintana G,

Daniele Antonucci, Francesco Babich, Cristian Pozza, Laura Maturi, Akshit Gupta

Projekt: EU LIFE BREEZE - Renovierungsmaßnahmen für emissionsfreie Gebäude

BREEZE (Building Renovation Efforts for Zero Emission Buildings) aims to accelerate the integration of solar photovoltaic (PV) systems into new and renovated buildings across EU Member States in line with the Energy Performance of Buildings Directive (EPBD). The project supports national implementation of the Solar Mandate by addressing key technical, financial, and regulatory barriers to large-scale PV deployment. BREEZE focuses on the effective use of rooftop PV, building-integrated photovoltaics (BIPV), and balcony PV to improve building energy performance while maintaining architectural quality. By developing standardized tools and practical frameworks, the project promotes cost-effective PV integration in renovation projects and strengthens the link between energy efficiency and on-site renewable generation. Pilot activities in Poland, Italy, and France will test PV deployment strategies under different building types, climates, and regulatory conditions, generating insights applicable across the EU. In parallel, BREEZE advances energy performance assessment methods to ensure compliance with EPBD targets and Indoor Environmental Quality (IEQ) standards. Through capacity-building activities, policy guidance, and best practice development, the project equips policymakers, designers, and industry professionals with the knowledge and tools needed to support a coordinated, sustainable, and scalable approach to solar energy integration in Europe’s building sector.

DOI

DOI: 10.4229/EUPVSEC2025/5DV.3.42

Journal article2025

BrickLLM: A Python library for generating Brick-compliant RDF graphs using LLMs

Perini M, Antonucci D, Giudice R, Piscitelli MS, Capozzoli A

Daniele Antonucci

Projekt: EU HEU MODERATE - MARKTFÄHIGE OFFENE DATENLÖSUNGEN FÜR OPTIMIERTE GEBÄUDEBEZOGENE ENERGIEDIENSTLEISTUNGEN

One of the key challenges of Energy Management and Information Systems in buildings is related to the lack of interoperability, due to the absence of standardization of the underlying data models. In recent years, there has been a growing interest in using ontology-based metadata models to address this issue, as they offer a structured approach to organize and share information across diverse systems (e.g. Brick ontology). However, the creation of ontology-based metadata models is often a labor-intensive task that requires specific domain expertise, hindering the practical use of such data models. For this reason, in this work the BrickLLM Python library is introduced, which addresses this issue by generating Brick-compliant Resource Description Framework graphs through Large Language Models, automating the process of converting natural language building descriptions into machine-readable metadata. The library supports both cloud-based APIs (e.g., OpenAI, Anthropic, Fireworks AI), local models (e.g. LLaMa3.2, etc.) and evenfine-tuned ones. This paper explores the architecture, key functionalities, and practical applications of BrickLLM, showcasing its potential impact on the future of building systems monitoring and automation.

DOI Quelle

DOI: 10.1016/j.softx.2025.102121

Inbook2024

Clustering of Building Stock

Prina MG, Filippi Oberegger U, Antonucci D, Ma Y, Shamsi MH, Sharifi M

Daniele Antonucci, Ulrich Filippi Oberegger, Matteo Prina

In Europe, buildings account for 40% of final energy demand. Building stock models assess the impacts of technologies on energy consumption, greenhouse gases, policies, city planning, renewable energy, renovation strategies, and health effects. Two approaches, top-down and bottom-up, generate these models using real data and simulations. In the big data era, information about buildings is increasingly available, allowing real analysis of building stocks (top-down approach). In the bottom-up approach, models are estimated through simulations of building archetypes and aggregated at stock level. Unsupervised machine learning like clustering is widely used to find and group similar buildings. Centroid- and density-based algorithms are most popular but subsequent evaluation of clusters is essential. In this chapter we demonstrate two applications of clustering on different building stocks. In the first, the aim is to generate heat saving cost curves for the residential sector. These curves allow policy makers to choose renovations that save most energy per Euro invested. In the second application, clustering is applied to a building stock in Flanders to generate synthetic data allowing to simulate energy efficiency scenarios for buildings. The archetypal modeling approach used classifies buildings based on characteristics and scales the energy consumption up to the entire housing stock.

DOI Quelle

DOI: 10.1007/978-3-031-47909-0_5

Journal article2024

Optimizing Energy Renovation in Building Portfolios: Approach and Decision-Making Platform

Castagna M, Somova O, Pozza C, De Michele G, Garzia F, Antonucci D, Pernetti R

Daniele Antonucci, Marco Castagna, Federico Garzia, Cristian Pozza, Olga Somova, Giuseppe De Michele

Projekt: IT BZ WAVE - Entwicklung innovativer integrierter Konzepte zur Gewährleistung eines hohen Niveaus an Energieeinsparung, Gesundheit und Sicherheit in Innenräumen bei der Sanierung des öffentlichen Gebäudebestands

The building sector contributes significantly to energy consumption and greenhouse gas emissions, with many buildings being energy inefficient. In response, the European Green Deal promotes improving energy efficiency to support decarbonization goals. However, managing energy consumption and integrating data from multiple sources presents challenges, especially for large building portfolios. This study introduces a novel methodology designed to optimize energy renovation strategies, balancing technical, financial, and maintenance considerations. The methodology is implemented in CERPlan 1.0, a web-based decision-support platform that combines data on building energy performance, renovation costs, and maintenance needs. Through simulations, CERPlan 1.0 helps decision-makers prioritize retrofit interventions based on economic criteria while leveraging synergies between energy improvements and regular maintenance. Application of this methodology to real estate portfolios reveals opportunities to enhance cost-effectiveness and energy savings. The results show that integrating maintenance into renovation planning reduces payback times and allows for more comprehensive renovation strategies. The conclusions highlight CERPlan 1.0’s potential to improve decision-making, making building renovations more efficient and sustainable.

DOI Quelle

DOI: 10.3390/en17225537

Inbook2024

Synthetic data on buildings

Antonucci D, Conselvan F, Mascherbauer P, Harringer D, Pozza C

Daniele Antonucci, Cristian Pozza

Projekt: EU HEU MODERATE - MARKTFÄHIGE OFFENE DATENLÖSUNGEN FÜR OPTIMIERTE GEBÄUDEBEZOGENE ENERGIEDIENSTLEISTUNGEN

The data-driven approach to building evaluation is gaining significant popularity. Policies related to the adoption of new energy-saving technologies are now rooted in their actual effects on reducing energy consumption. The pandemic and subsequent war have emphasized the crucial significance of various building aspects, including air quality, indoor comfort (thermal, light, and acoustic), and energy efficiency. Evaluating these aspects necessitates the use of real data, which, unfortunately, is often inaccessible, of low quality, or incomplete. Yet, even in the energy and building fields, data anonymization methods like normalization and aggregation limit the information that can be shared. Therefore, finding more effective ways to collaborate on data without compromising privacy is critical for both data owners and data analysts. According to the European Commission’s Joint Research Centre, synthetic data will play a crucial role in enabling AI. This type of data serves as a unifying bridge between policy support and computational models, unlocking the potential of data that may be hidden in silos. Consequently, it becomes the primary catalyst for AI adoption in business and policy applications across Europe. Additionally, the resulting data not only can be freely shared but also aids in rebalancing under-represented classes in research studies through over-sampling, making it an ideal input for machine learning and AI models. One of the fundamental elements involves developing AI models that can accurately and faithfully reproduce various types of building data, considering factors such as climatic conditions, building and system types, occupancy profiles, and intended use. The present study aims to evaluate potential scenarios and applications of AI models for generating valuable data for both building energy assessments and economic evaluations.

DOI Quelle

DOI: 10.1007/978-3-031-47909-0_7

Journal article2017

Building performance evaluation through a novel feature selection algorithm for automated arx model identification procedures

Antonucci D, Filippi Oberegger U, Pasut W, Gasparella A

Daniele Antonucci, Ulrich Filippi Oberegger

ARX models are an effective instrument to evaluate continuous building performance from insufficient monitoring data. However, selecting the right model features is NP-hard. The problem of finding a minimal subset of informative inputs has been studied extensively in various fields but automatic, fast, and reliable procedures for finding optimal models for building performance evaluation are still missing. We propose a novel feature selection algorithm named Greedy Correlation Screening (GCS), which identifies a possible solution at a time by greedily maximizing the correlation between inputs and output and minimizing cross-correlations between inputs. These two objectives are competing, thus leading to best tradeoffs. Among these, the best model is automatically selected by applying filters and quality criteria such as the adjusted coefficient of correlation and non-correlation of residuals.

The performance of the proposed heuristic method is compared to two of the best algorithms used in the field, such as GRASP for feature selection and NSGA-II (Non-dominated Sorting Genetic Algorithm). The application on a real case study demonstrates that the proposed method solves the problem of feature selection in building performance estimation efficiently and reliably. Moreover, the model creation is automatic, making it ideal for integration into a Building Management System (BMS) in order to detect faults and perform short-term predictive control.

DOI Quelle

DOI: 10.1016/j.enbuild.2017.06.009

Technische
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Inbook2024

Synthetic data on buildings

Antonucci D, Conselvan F, Mascherbauer P, Harringer D, Pozza C

Daniele Antonucci, Cristian Pozza

Projekt: EU HEU MODERATE - MARKTFÄHIGE OFFENE DATENLÖSUNGEN FÜR OPTIMIERTE GEBÄUDEBEZOGENE ENERGIEDIENSTLEISTUNGEN

DOI Quelle

DOI: 10.1007/978-3-031-47909-0_7

Daniele Antonucci

About

Scientific Publications

BREEZE Project: Advancing PV Implementation in Building Renovations

BrickLLM: A Python library for generating Brick-compliant RDF graphs using LLMs

Clustering of Building Stock

Optimizing Energy Renovation in Building Portfolios: Approach and Decision-Making Platform

Synthetic data on buildings

Building performance evaluation through a novel feature selection algorithm for automated arx model identification procedures

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Externe Inhalte

Scientific Publications

BREEZE Project: Advancing PV Implementation in Building Renovations

BrickLLM: A Python library for generating Brick-compliant RDF graphs using LLMs

Clustering of Building Stock

Optimizing Energy Renovation in Building Portfolios: Approach and Decision-Making Platform

Synthetic data on buildings

Building performance evaluation through a novel feature selection algorithm for automated arx model identification procedures