Delft AI Energy Lab
AI for sustainable, reliable and effective energy systems
Energy systems are the backbone of our modern society. It is of great importance that these systems are sustainable, reliable and effective now and in the future. There is strong expertise in this field on the TU Delft campus. The Delft AI Energy Lab investigates how new AI-based methods can contribute to the management of dynamic energy systems.
Therefore we combine groundbreaking machine learning with the reliable theory of the physical energy system. For example, it is possible with the AI technique 'neural networks' to model differential equations describing dynamics in areas such as fluid dynamics, and for predicting extreme, rare events. Delft AI Energy Lab investigates these promising methods for applicability for monitoring the 'health' of parts of energy systems, and for the early detection of threats.
The Delft AI Energy Lab is part of the TU Delft AI Labs programme.
The team
PhD candidates
Perine Cunat
PhD researcher
Postdocs
Education
Courses
2023/2024
2022/2023
- Machine Learning for Graph Data | CS4350
- Modeling Uncertainty and Data for Engineers | CEGM1000
2020/2021
- Applied Machine Learning | CS4305TU
- Research skills 1 | CIE5431
- Mutimedia Analysis | CSE2230
2019/2020
-
Signal Processing | CSE2220
Resources
Master projects
Ongoing
- Adaptive Learning on graphs, Elvin Isufi, Alex Jeleniewski (2023/2024)
- Graph Learning for Multi-Sensor Radar, Elvin Isufi, Radu Gaghi (2023/2024)
- Graph Neural Networks for Renewable Energy, Elvin Isufi, Rodrigo Revilla Llaca (2023/2024)
- Hybrid Modelling in Hydrology Using a Neural Ordinary
- Differential Equations Approach, Riccardo Taormina, Jonathan Schieren (2023/2024)
- Graph Neural Networks for Predicting Dike-breach floods, Riccardo Taormina, Sergio Bulte (2023/2024)
- Small Deep Learning Models for Sewer Defect Detection, Riccardo Taormina, Brendan Determan (2023/2024)
- Improving PIV-based streamflow estimation with Deep Learning, Riccardo Taormina, Max Helmich (2023/2024)
- GAN-based rainfall nowcasting, Riccardo Taormina, Sven van Os (2023/2024)
- Optimizing the pump schedule of water distribution systems using a deep learning metamodel, Riccardo Taormina, Nikolaos Mertzanis (2022/2023)
Finished
- GGANet: Algorithm Unrolling for Water Distribution Networks Metamodelling, Riccardo Taormina, Albert Roca Solà (2023/2024)
- Sparse & Interpretable Graph Attention Networks, Elvin Isufi, Titus Naber (2023/2024)
- An Experimental Assessment of the Stability of Graph Contrastive Learning, Elvin Isufi, Siert Sebus (2023/2024)
- Bayesian Contrastive Learning on Topological Structures, Elvin Isufi, Alex Mollers (2023/2024)
- Assessment of Pump Failures in Rotterdam: A Five-Year Study (2016-2020): A Failure Analysis based on statistical modelling, Riccardo Taormina, Qiwen Zhang (2023/2024)
- Diverse Explorations of Rainfall Nowcasting with TrajGRU: Mitigating Smoothness and Fading Out Challenges for Longer Lead Times, Riccardo Taormina, Yanghuan Zou (2023/2024)
- Towards a fully distributed multivariable hydrological deep learning model with graph neural networks, Riccardo Taormina, Peter Nelemans (2023/2024)
- Characterization of plastic transport in the Saigon River: An analysis of the river stretch that crosses Ho Chi Minh City conducted in the rainy season., Riccardo Taormina, Francesca Lena (2023/2024)
- Characterization of plastic transport in the Saigon River: An analysis of the river stretch that crosses Ho Chi Minh City conducted in the rainy season., Riccardo Taormina, Edoarto Forte (2023/2024)
- Characterization of plastic transport in the Saigon River: An analysis of the river stretch that crosses Ho Chi Minh City conducted in the rainy season., Riccardo Taormina, Agatha Zamuner (2023/2024)
- Prediction of Discharges from Polders to ‘Boezem’ Canals with a Random Forest and an LSTM Model: Improving Inputs of the Decision Support System of the Hoogheemraadschap van Delfland, Riccardo Taormina, Josine van Marrewijk (2023/2024)
- The Hierarchical Subspace Iteration Method for Computing Vibration Modes of Elastic Objects, Elvin Isufi, Julian van Dijk (2023/2024)
- A System for Model Diagnosis centered around Human Computation, Elvin Isufi, Ziad Ahmad Saad Soliman Nawar (2023/2024)
- Automatic feature discovery: A comparative study between filter and wrapper feature selection techniques, Elvin Isufi, Andrei Mân?stireanu (2023/2024)
- Encoding methods for categorical data: A comparative analysis for linear models, decision trees, and support vector machines, Elvin Isufi, Andrei Udil? (2023/2024)
- Filtering Knowledge: A Comparative Analysis of Information-Theoretical-Based Feature Selection Methods, Elvin Isufi, Kiril Vasilev (2023/2024)
- Data-Driven Empirical Analysis of Correlation-Based Feature Selection Techniques, Elvin Isufi, Florena Bu?e (2023/2024)
- Perceptual losses in precipitation nowcasting, Riccardo Taormina, Diewertje Dekker (2022/2023)
- Development of an LSTM-based methodology for burst detection in water distribution systems, Riccardo Taormina, Konstantinos Glynis (2022/2023)
- The role of water vapor observations in satellite-based rainfall information highlighted by a Deep Learning approach, Riccardo Taormina, Fabo Curzi (2022/2023)
- Predicting fluvial flood arrival times by making use of a deep learning model, Riccardo Taormina, Ron Bruijns (2022/2023)
- Using YOLOv5 for the Detection of Icebergs in SAR Imagery, Riccardo Taormina, Daan Hulskemper (2022/2023)
- Assessing the applicability of Transformer-based architectures as rainfall-runoff models, Riccardo Taormina, Kangmin Mao (2022/2023)
- Simplicial Unrolling Elastic Net for Edge Flow Signal Reconstruction, Elvin Isufi, Chengen Liu (2022/2023)
- From Clicks to Conscious Choices: Investigating the Effects of Carbon Footprint Data in E-Commerce Recommender Systems, Elvin Isufi, Sneha Lodha (2022/2023)
- Graph Reqularized Tensor Decomposition for Recommender Systems, Elvin Isufi, Rohan Chandrashekar (2022/2023)
- Pure Cold Start Recommendation by Learning on Stochastically Expanded Graphs, Elvin Isufi, Simon Dahrs (2022/2023)
- Nudging Towards Sustainable Choices via Recommender Systems, Elvin Isufi, Raoul Kalisvaart (2022/2023)
- Deep Learning for Geotechnical Engineering: The Effectiveness of Generative Adversarial Networks in Subsoil Schematization, Riccardo Taormina, Fabian Campos Montero (2022/2023)
- Quantum to Transport: Modeling Transport Properties of Aqueous Potassium Hydroxide by Machine Learning Molecular Force Fields from Quantum Mechanics, Riccardo Taormina, Jelle Lagerweij (2022/2023)
- A LSTM-based Generative Adversarial Network for End-use Water Modelling, Riccardo Taormina, Yukun Xie (2022/2023)
- Operational Streamflow Drought Forecasting for the Rhine River at Lobith Using the LSTM Deep Learning Approach, Riccardo Taormina, Jing Deng (2022/2023)
- cGANs for multispectral snow extent analysis in the Alps, Riccardo Taormina, Adriaan Keurhorst (2022/2023)
- The Effect of Climate Variability on the Root Zone Storage Capacity, Riccardo Taormina, Nienke Tempel (2022/2023)
- GNNs and Beam Dynamics: Investigation into the application of Graph Neural Networks to predict the dynamic behaviour of lattice beams, Riccardo Taormina, Lex Niessen (2022/2023)
- The impact of an additional phenology model on the performance of conceptual hydrological models, Riccardo Taormina, Casper Pierik (2022/2023)
- Leak Localization in Water Distribution Networks, Riccardo Taormina, Zixi Meng (2022/2023)
- Macrolitter in Groyne Fields: Short term variability & the influence of natural processes, Riccardo Taormina, Jakob Grosfeld (2022/2023)
- Water balance-based approach to improve understanding of Drought Development: by calculating the root storage deficit, Riccardo Taormina, Piet Storm (2022/2023)
- APDUDS, Riccardo Taormina, Max Lange (2022/2023)
- Improving APDUDS, Riccardo Taormina, Jip Steiger (2022/2023)
- Self-Supervised Few Shot Learning: Prototypical Contrastive Learning with Graphs, Elvin Isufi, Ojas Shirekar (2022/2023)
- Hardware-based implementations in Side-Channel Analysis: A comparison study of DL SCA attacks against HW and SW AES and a novel methodology, Elvin Isufi, Wolf Bubberman (2022/2023)
- Assessing Global Applicability of a Long Short-Term Memory (LSTM) Neural Network for Rainfall-Runoff Modelling, Riccardo Taormina, Katharina Wilbrand (2021/2022)
- Short-term Water Demand Forecasting at a District Level Using Deep Learning Techniques, Riccardo Taormina, Diego Mauricio Corredor Mora (2021/2022)
- Applying deep learning vs machine learning models to reproduce dry spells at point scale from satellite information in a data-scarce region: the case of northern Ghana, Riccardo Taormina, Panagiotis Mavritsakis (2021/2022)
- Exploration of Deep Learning-based Computer Vision for the detection of floating plastic debris in waterways, Riccardo Taormina, Andé J. Vallendar (2021/2022)
- Nowcasting heavy precipitation in the Netherlands: a deep learning approach, Riccardo Taormina, Eva van der Kooij (2021/2022)
- Deep Statistical Solver for Distribution System State Estimation, Elvin Isufi, Benjamin Habib (2021/2022)
- Do multi-year droughts increase floods?, Riccardo Taormina, Yang Zhao (2021/2022)
- Automatic Generation of Water Distribution Systems, Riccardo Taormina, Dimitri Tijdeman (2021/2022)
- Short-term Earthquake Prediction with Deep Neural Networks: Finding the optimal time prior to earthquake strikes to use in predictions, Elvin Isufi, Glenn van den Belt (2021/2022)
- Improving cell type matching across species in scRNA-seq data using protein embeddings and transfer learning, Elvin Isufi, Kirti Biharie (2021/2022)
- Tikhonov and Sobolev regularisers compared to user-based KNN collaborative filtering, Elvin Isufi, Sérénic Monté (2021/2022)
- Total Variation Regularisation for Item KNN Collaborative Filtering: Performance Analysis, Elvin Isufi, Lars van Blokland (2021/2022)
- The Performance of Total Variation Regularizer for User Collaborative Filtering, Elvin Isufi, Karolis Mari?nas (2021/2022)
- Item-Item Collaborative Filtering via Graph Regularization, Elvin Isufi, Melle Jansen (2021/2022)
- Impact of seismic wave length to detect high-magnitude earthquakes via deep learning, Elvin Isufi, Gancho Georgiev (2021/2022)
- How long before strike can we predict earthquakes with an LSTM neural network?, Elvin Isufi, Amaury Charlot (2021/2022)
- Impact of Focal Depth on Short-Term Earthquake Prediction using Deep Learning, Elvin Isufi, Pijus Krisiuk?nas (2021/2022)
- Predicting Micro-Earthquakes with Deep Neural Networks, Elvin Isufi, Kevin Zhu (2021/2022)
- Short-term Earthquake Prediction via Recurrent Neural Network Models: Comparison among vanilla RNN, LSTM and Bi-LSTM, Elvin Isufi, Xiangyu Du (2021/2022)
- Earthquake Prediction: A MLP & SVM Comparison, Elvin Isufi, Daniel van den Akker (2021/2022)
- Comparing multichannel mixed CNN-RNN to individual models for earthquake prediction, Elvin Isufi, Maikel Houbaer (2021/2022)
- How does a CNN mixed with LSTM methods compare with the individual one in predicting earthquakes?, Elvin Isufi, Irtaza Hashmi (2021/2022)
- Parametric design of a grid shell roof over existing buildings?, with a focus on connection design, Elvin Isufi, Fiori Isufi (2021/2022)
- Investigation of focal epilepsy using graph signal processing, Elvin Isufi, Gaia Zin (2021/2022)
- Assessing the Capability of Multimodal Variational Auto-Encoders in Combining Information From Biological Layers in Cancer Cells, Elvin Isufi, Bram Pronk (2021/2022)
- Benchmarking VAE latent features in downstream tasks for cancer related predictions, Elvin Isufi, Boris van Groeningen (2021/2022)
- The Effect of Different Initialization Methods on VAEs for Modeling Cancer using RNA Genome Expressions, Elvin Isufi, Ivo Kroskinski (2021/2022)
- Benchmarking the hyper-parameter sensitivity of VAE models for cancer treatment, Elvin Isufi, Armin Korki? (2021/2022)
- Finding disentangled representations using VAE, Elvin Isufi, Raymond d'Anjou (2021/2022)
- Creation of new Extra-Tropical Cyclone fields in the North Atlantic with Generative Adversarial Networks, Riccardo Taormina, Filippo Dainelli (2020/2021)
- Side-channel analysis with graph neural networks, Elvin Isufi, Vasco de Bruijn (2020/2021)
- Matching streamflow river gauges with hydrologic models, Riccardo Taormina, Mizzi van der Ven (2020/2021)
- Integrated Neural Network and Finite Element Analysis for constitutive modelling of soil, Riccardo Taormina, Keshav Kashichenula (2020/2021)
- 3D Road Boundary Mapping of MLS Point Clouds, Riccardo Taormina, Qian Bai (2020/2021)
- Shallow Cumulus Clouds as Complex Networks, Riccardo Taormina, Pouriya Alinaghi (2020/2021)
- The variability of the rootzone storage capacity in Austria: An exploration of its controls, Riccardo Taormina, Bart Veenings (2020/2021)
- Relating groundwater heads to stream discharge by using machine learning techniques: A case study in subcatchment Chaamse Beken, Riccardo Taormina, Valerie Demetriades (2020/2021)
- Mathematical framework to understand better the behavior of the graphs convolutional neural network to random perturbations, Alejandro Ribeiro, Gao Zhan (2020/2021)
- Combining frequency information and the unsupervisedW-Net model for wheat head detection, Elvin Isufi, Ivo Chen (2020/2021)
- Improving the Performance of Object Counting Using Training Images in the Frequency Domain, Elvin Isufi, Dani Rogmans (2020/2021)
- Using frequency information to improve accuracy of object detectors, Elvin Isufi, Petar Ulev (2020/2021)
- Injecting prior frequency information in DETR for wheat head detection, Elvin Isufi, Alin Prundeanu (2020/2021)
- Accelerating Axial-Symmetrical Nebulae Visualization and Reconstruction, Elvin Isufi, Nouri Khalass (2020/2021)
- Accuracy-Diversity Trade-off in Recommender Systems Via Graph Convolutions, Elvin Isufi, Matteo Pocchiari (2019/2020)
- Identifying Author Fingerprints in Texts via Graph Neural Networks, Elvin Isufi, Tomas Sipko (2019/2020)
- Graph-Adaptive Activation Functions for Graph Neural Networks, Elvin Isufi, Bianca Iancu (2019/2020)
- Accuracy-Diversity Trade-off in Recommender Systems Via Graph Convolutions, Elvin Isufi, M. Pocchaiari (2019/2020)
- Automatic Depth Matching for Petrophysical Borehole Logs, Elvin Isufi, A. Garcia Manso (2019/2020)
- Visually grounded fine-grained speech representations learning, Elvin Isufi, Tian Tian (2019/2020)
- Advances in Graph Signal Processing: Fast graph construction & Node-adaptive graph signal reconstruction, Elvin Isufi, Maosheng Yang (2019/2020)
- Graph-Time Convolutional Neural Network: Learning from Time-Varying Signals defined on Graphs, Elvin Isufi, Gabriele Mazzola (2019/2020)
- Applying Machine Learning to Learn System Dynamics Models for Urban Systems, Elvin Isufi, Rukai Yin (2019/2020)
- Designing an escape room sensory system: S.C.I.L.E.R.: sensory communication inside live escape rooms, Elvin Isufi, Issa Hanou (2019/2020)
- Designing an escape room sensory system: S.C.I.L.E.R.: sensory communication inside live escape rooms, Elvin Isufi, Gwennan Smitskamp (2019/2020)
- Designing an escape room sensory system: S.C.I.L.E.R.: sensory communication inside live escape rooms, Elvin Isufi, Marijn de Schipper (2019/2020)
- Active Semi-Supervised Learning For Diffusions on Graphs, Elvin Isufi, Biswadeep Das (2019/2020)
- Interpreting Information of Deep Neural Networks for Profiled Side Channel Analysis, Elvin Isufi, Marius Pop (2019/2020)
- Blind Graph Topology Change Detection: A Graph Signal Processing approach, Elvin Isufi, Ashvant Mahabir (2019/2020)
- Can fourier neural operators replicate the intrinsic predictability of spatiotemporal chaos?: for the Kuramoto-Sivashinsky system, Riccardo Taormina, Kevin Schuurman ()
- Estimating new reservoir locations with the use of a hydrological model for small holder cotton farmers in Maharashtra, India, Riccardo Taormina, Jente Janssen ()