Narada Dilp Warakagoda
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View article: Semi-Data-Driven Model Predictive Control: A Physics-Informed Data-Driven Control Approach
Semi-Data-Driven Model Predictive Control: A Physics-Informed Data-Driven Control Approach Open
Data-enabled predictive control (DeePC) has emerged as a powerful technique to control complex systems without the need for extensive modeling efforts. However, relying solely on offline collected data trajectories to represent the system …
View article: Deep and Probabilistic Solar Irradiance Forecast at the Arctic Circle
Deep and Probabilistic Solar Irradiance Forecast at the Arctic Circle Open
Solar irradiance forecasts can be dynamic and unreliable due to changing weather conditions. Near the Arctic circle, this also translates into a distinct set of further challenges. This work is forecasting solar irradiance with Norwegian d…
View article: A Unified Graph Formulation for Spatio-Temporal Wind Forecasting
A Unified Graph Formulation for Spatio-Temporal Wind Forecasting Open
With the rapid adoption of wind energy globally, there is a need for accurate short-term forecasting systems to improve the reliability and integration of such energy resources on a large scale. While most spatio-temporal forecasting syste…
View article: It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting
It is all Connected: A New Graph Formulation for Spatio-Temporal Forecasting Open
With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future. Most spatio-temporal forecasting models typically comprise distinc…
View article: Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures
Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures Open
To improve the security and reliability of wind energy production, short-term forecasting has become of utmost importance. This study focuses on multi-step spatio-temporal wind speed forecasting for the Norwegian continental shelf. In part…
View article: Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks
Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks Open
The rapid depletion of fossil-based energy supplies, along with the growing reliance on renewable resources, has placed supreme importance on the predictability of renewables. Research focusing on wind park power modelling has mainly been …
View article: Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures
Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures Open
This study focuses on multi-step spatio-temporal wind speed forecasting for the Norwegian continental shelf. The study aims to leverage spatial dependencies through the relative physical location of different measurement stations to improv…
View article: Challenges of Labelling Unknown Seabed Munition Dumpsites from Acoustic and Optical Surveys: A Case Study at Skagerrak
Challenges of Labelling Unknown Seabed Munition Dumpsites from Acoustic and Optical Surveys: A Case Study at Skagerrak Open
The disposal of unexploded ordnance (UXOs) at sea is a global problem. The mapping and remediation of historic UXOs can be assisted by autonomous underwater vehicles (AUVs) carrying sensor payloads such as synthetic aperture sonar (SAS) an…
View article: Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses
Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses Open
With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn complex patterns in the …
View article: Automatic recognition of underwater munitions from multi-view sonar surveys using semi supervised machine learning: a simulation study
Automatic recognition of underwater munitions from multi-view sonar surveys using semi supervised machine learning: a simulation study Open
This paper presents a machine learning technique for using large unlabelled survey datasets to aid automatic classification. We have demonstrated the benefit of this technique on a simulated synthetic aperture sonar (SAS) dataset. We desig…
View article: Seafloor Pipeline Detection With Deep Learning
Seafloor Pipeline Detection With Deep Learning Open
This paper presents fast, accurate, and automatic methods for detecting seafloor pipelines in multibeam echo sounder data with deep learning. The proposed methods take inspiration from the highly successful ResNet and YOLO deep learning mo…
View article: Generative Adversarial Immitation Learning for Steering an Unmanned Surface Vehicle
Generative Adversarial Immitation Learning for Steering an Unmanned Surface Vehicle Open
The task of obstacle avoidance using maritime vessels, such as Unmanned Surface Vehicles (USV), has traditionally been solved using specialized modules that are designed and optimized separately. However, this approach requires a deep insi…