Innovative methodologies for the design of Zero-Emission and cost-effective Buildings enhanced by Artificial Intelligence

Climate, Energy & MobilityHORIZON-IAID: 101138678
EC Contribution
€38,792
Consortium Size
18 orgs
Start Year
2024
Summary

ZEBAI is an ambitious integrative project in which a broad range of interdisciplinary teams collaborate to develop a new methodology that aims to change the way that Zero-emission buildings are designed, by integrating all interdependent analysis and partial alternative decision-making processes under a holistic approach that allows the evaluation of a design simultaneously taking into account: energy performance, environmental impact, indoor environmental quality, and cost-effectiveness. For this purpose, we will require to develop a database of well-characterised materials and make an estimation of discrepancies between simulated and actual building performance. The methodology that will be used is artificial intelligence techniques to optimise the selection of materials and systems in different aspects of the building design. The AI-assisted methodology aims to make the design process more efficient and user-friendly while incorporating all environmental quality and cost-effectiveness objectives. This approach will enable the optimisation of new architectural designs towards scalable Zero Energy Building (ZEB) design in different climates, usages, and building patterns, with the ultimate goal of achieving a zero-emission building stock by 2050. During the project, we will test ZEBAI methodology with four representative demonstrators (located in Ukraine, Spain, the United Kingdom, and the Netherlands). ZEBAI relies on previously funded European research projects and aligns with several national initiatives in which the partners collaborate.

Consortium (18)

Project Results (26)

Source: CORDIS, the EU research results database.

Publications (19)
A transformer-based time series forecasting model with an efficient data preprocessing scheme
Bulletin of Electrical Engineering and Informatics· 2025DOI
Kyrylo Yemets, Michal Gregus
Conditional generative AI for high-fidelity synthesis of hydrating cementitious microstructures
Materials & Design· 2025DOI
Minfei Liang, Kun Feng, Jinbao Xie, Yuyang Wei, Sonia Contera, Erik Schlangen, Branko Šavija
Enhancing the FFT-LSTM Time-Series Forecasting Model via a Novel FFT-Based Feature Extraction–Extension Scheme
Big Data and Cognitive Computing· 2025DOI
Kyrylo Yemets, Ivan Izonin, Ivanna Dronyuk
High-Fidelity Synthetic Data Generation Framework for Unique Objects Detection
computation· 2025DOI
Shakhovska, Natalya; Sydor, Bohdan; Liaskovska, Solomiya; Duran, Olga; Martyn, Yevgen; Віра, Володимир
Multi-family wavelet-based feature engineering method for short-term time series forecasting
Scientific Reports· 2025DOI
Kyrylo Yemets, Ivan Izonin, Stergios Aristoteles Mitoulis
Nature-inspired swarm optimization paradigms for securing semantic web frameworks against DDoS attacks: a computational approach
Scientific Reports· 2025DOI
Chirag Ganguli, Shishir Kumar Shandilya, Ivan Izonin, Lesia Hentosh
Regression-based Model for Predicting Simulated vs Actual Building Performance Discrepancies
Procedia Computer Science· 2025DOI
Ivan Izonin, Roman Tkachenko, Rosana Caro, Antonio LaTorre de la Fuente, Kyrylo Yemets, Stergios Aristoteles Mitoulis
Site Evaluation and Data Modeling for Renewable Energy Integration: A Case Study in Seville, Spain
WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT· 2025DOI
Christos Pavlatos
Systematic Generation and Evaluation of Synthetic Production Data for Industry 5.0 Optimization
Technologies· 2025DOI
Solomiia Liaskovska, Sviatoslav Tyskyi, Yevgen Martyn, Andy T. Augousti, Volodymyr Kulyk
TCN-QRNN model for short term energy consumption forecasting with increased accuracy and optimized computational efficiency
Scientific Reports· 2025DOI
Lesia Mochurad, Roman Levkovych
Time Series Forecasting Model Based on the Adapted Transformer Neural Network and FFT-Based Features Extraction
Sensors· 2025DOI
Kyrylo Yemets, Ivan Izonin, Ivanna Dronyuk
Advancements in AI-Based Information Technologies: Solutions for Quality and Security
Systems· 2024DOI
Tetiana Hovorushchenko; Ivan Izonin; Hakan Kutucu
Multi-Step Dynamic Ensemble Selection to Estimate Software Effort
Applied Artificial Intelligence· 2024DOI
Akshay Jadhav; Shishir Kumar Shandilya; Ivan Izonin; Roman Muzyka
Quality and Security of Critical Infrastructure Systems
Big Data and Cognitive Computing· 2024DOI
Ivan Izonin, Tetiana Hovorushchenko, Shishir Kumar Shandilya
Scientific Reports
Scientific Reports· 2024DOI
Ivan Izonin, Illia Nesterenko, Athanasia K. Kazantzi, Roman Tkachenko, Roman Muzyka, Stergios Aristoteles Mitoulis
GRNN-based cascade ensemble model for non-destructive damage state identification small data approach
Engineering with ComputersDOI
Izonin, Ivan; Kazantzi, Athanasia; Tkachenko, Roman; MITOULIS, STERGIOS ARISTOTELES
Real-Time Monitoring of Road Networks for Pavement Damage Detection Based on Preprocessing and Neural Networks
Big Data and Cognitive ComputingDOI
Shakhovska, Natalya; Yakovyna, Vitaliy; Mysak, Maksym; MITOULIS, STERGIOS ARISTOTELES; Argyroudis, Sotirios; Syerov, Yuriy
Scientific Reports
Scientific ReportsDOI
Shakhovska, Natalya; Mochurad, Lesia; Caro, Rosana; Argyroudis, Sotirios
Sensors
SensorsDOI
Izonin, Ivan; Muzyka, Roman; Tkachenko, Roman; Dronyuk, Ivanna; Yemets, Kyrylo; MITOULIS, STERGIOS ARISTOTELES
Deliverables (6)
Other Results (1)
Periodic Reporting for period 1 - ZEBAI (Innovative methodologies for the design of Zero-Emission and cost-effective Buildings enhanced by Artificial Intelligence)