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A bivariate ensemble model output statistics (EMOS) technique for the postprocessing of ensemble forecasts of two-dimensional wind vectors is proposed, where the postprocessed probabilistic forecast takes the form of a bivariate normal…

Applications · Statistics 2015-06-03 Nina Schuhen , Thordis L. Thorarinsdottir , Tilmann Gneiting

Mobile edge computing (MEC) and wireless power transfer (WPT) are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things.…

Signal Processing · Electrical Eng. & Systems 2018-07-19 Fuhui Zhou , Yongpeng Wu , Rose Qingyang Hu , Yi Qian

Wind and solar PV generation data for the entire contiguous US are calculated, on the basis of 32 years of weather data with temporal resolution of one hour and spatial resolution of 40x40km$^2$, assuming site-suitability-based as well as…

Aligning large language models with human feedback at inference time has received increasing attention due to its flexibility. Existing methods rely on generating multiple responses from the base policy for search using a reward model,…

Computation and Language · Computer Science 2026-03-17 Yige Yuan , Teng Xiao , Li Yunfan , Bingbing Xu , Shuchang Tao , Yunqi Qiu , Huawei Shen , Xueqi Cheng

We consider a sensing application where the sensor nodes are wirelessly powered by an energy beacon. We focus on the problem of jointly optimizing the energy allocation of the energy beacon to different sensors and the data transmission…

Information Theory · Computer Science 2018-06-07 Ayca Ozcelikkale , Mehmet Koseoglu , Mani Srivastava

Ambitious decarbonisation targets are rapidly increasing the commission of new offshore wind farms. For these newly commissioned plants to run, accurate power forecasts are needed from the onset. These allow grid stability, good reserve…

Machine Learning · Computer Science 2026-04-27 Dominic Weisser , Chloé Hashimoto-Cullen , Benjamin Guedj

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather…

Applications · Statistics 2022-04-26 Kaleb Phipps , Sebastian Lerch , Maria Andersson , Ralf Mikut , Veit Hagenmeyer , Nicole Ludwig

The image quality of the new generation of earthbound Extremely Large Telescopes (ELTs) is heavily influenced by atmospheric turbulences. To compensate these optical distortions a technique called adaptive optics (AO) is used. Many AO…

Numerical Analysis · Mathematics 2020-09-03 Bernadett Stadler , Roberto Biasi , Mauro Manetti , Ronny Ramlau

Energy Efficiency (EE) is a big issue in 5th Generation Wireless Communications (5G) on condition that the number of access User Equipments (UEs) are exploding and more antennas should be equipped in one Base Station (BS). In EE studies,…

Information Theory · Computer Science 2015-04-21 Di Zhang , Zhenyu Zhou , Keping Yu , Takuro Sato

Wind farms are a crucial driver toward the generation of ecological and renewable energy. Due to their rapid increase in capacity, contemporary wind farms need to adhere to strict constraints on power output to ensure stability of the…

Optimization of rotating electrical machines is both time- and computationally expensive. Because of the different parametrization, design optimization is commonly executed separately for each machine technology. In this paper, we present…

Machine Learning · Computer Science 2023-08-25 Vivek Parekh , Dominik Flore , Sebastian Schöps

Wind energy plays a critical role in the transition towards renewable energy sources. However, the uncertainty and variability of wind can impede its full potential and the necessary growth of wind power capacity. To mitigate these…

Machine Learning · Computer Science 2026-01-13 Stefan Jonas , Kevin Winter , Bernhard Brodbeck , Angela Meyer

In this paper, we examine the maximization of energy efficiency (EE) in next-generation multi-user MIMO-OFDM networks that evolve dynamically over time - e.g. due to user mobility, fluctuations in the wireless medium, modulations in the…

Information Theory · Computer Science 2015-04-16 Panayotis Mertikopoulos , E. Veronica Belmega

In this paper, we study the performance of non-orthogonal multiple access (NOMA) schemes in wireless powered communication networks (WPCN) focusing on the system energy efficiency (EE). We consider multiple energy harvesting user equipments…

Information Theory · Computer Science 2018-06-19 Tewodros A. Zewde , M. Cenk Gursoy

This paper studies the maximization of the weighted sum energy efficiency (WSEE). We derive a first-order optimal algorithm applicable to a wide class of communication scenarios exhibiting very fast convergence. We also discuss how to…

Information Theory · Computer Science 2018-12-19 Bho Matthiesen , Yang Yang , Eduard A. Jorswieck

This paper surveys the primary computational hurdles of Energy Systems optimization coming from different sources: model-induced complexity, optimization algorithm requirements, and uncertainties handling (both aleatoric and epistemic).…

Validating engineering wake models under real-world operational conditions is essential for improving wind farm performance predictions. This study uses a unique dataset from the Lillgrund offshore wind farm, collected during the Horizon…

With the development of offshore wind farms (OWFs) in far-offshore and deep-sea areas, each OWF could contain more and more wind turbines and cables, making it imperative to study high-reliability electrical collector system (ECS) for OWF.…

Systems and Control · Electrical Eng. & Systems 2023-04-11 Xiaochi Ding , Xinwei Shen , Qiuwei Wu , Liming Wang , Dechang Yang

The paper contains the concept and the results of research concerning the evolutionary algorithm, identified based on the systems control theory, which was called the Systemically of Evolutionary Algorithm (SAE). Special attention was paid…

Neural and Evolutionary Computing · Computer Science 2014-01-24 Jerzy Tchorzewski , Emil Chyzy

Ensemble learning is a meta-learning approach that combines the predictions of multiple learners, demonstrating improved accuracy and robustness. Nevertheless, ensembling models like Convolutional Neural Networks (CNNs) result in high…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-09-16 Le Zhang , Onat Gungor , Flavio Ponzina , Tajana Rosing