A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Method reconstructs missing wind farm data using graph theory and nearest neighbors.
Wind farm layout optimisation tackles space constraints with Bayesian multi-objective approach.
New method predicts wind farm power and wakes using weather patterns.
Bayesian method models multivalued power data from wind farms.
Optimal wind farm placement using quantile constraints for better power output.
The study optimizes wind farm yaw control using Gaussian process regression and high-fidelity simulations.
We introduce a measure for estimating the best risk-return relation of power production in wind farms within a given time-lag, conditioned to the velocity field. The velocity field is represented by a scalar that weighs the influence of the velocity at each wind turbine at present and previous time-steps for the presen…
The installation process of offshore wind turbines requires the use of expensive jack-up vessels. These vessels regularly report their position via the Automatic Identification System (AIS). This paper introduces a novel approach of applying machine learning to AIS data from jack-up vessels. We apply the new method to …
Wind power, as an alternative to burning fossil fuels, is abundant and inexhaustible. To fully utilize wind power, wind farms are usually located in areas of high altitude and facing serious ice conditions, which can lead to serious consequences. Quick detection of blade ice accretion is crucial for the maintenance of …
Multi-agent coordination is prevalent in many real-world applications. However, such coordination is challenging due to its combinatorial nature. An important observation in this regard is that agents in the real world often only directly affect a limited set of neighbouring agents. Leveraging such loose couplings amon…
Study short-term wind power and speed predictions using machine learning.
Extends neural diffusion processes for multi-task regression.
Proposes a model combining graph networks and variational Bayes for graph data.
CESAR improves wind speed and power forecasting for high-resolution simulations.
Novel framework improves wind power forecasts by bundling assets and using machine learning.
Transfer Learning (TL) in Deep Neural Networks is gaining importance because in most of the applications, the labeling of data is costly and time-consuming. Additionally, TL also provides an effective weight initialization strategy for Deep Neural Networks . This paper introduces the idea of Adaptive Transfer Learning …
In many settings, as for example wind farms, multiple machines are instantiated to perform the same task, which is called a fleet. The recent advances with respect to the Internet of Things allow control devices and/or machines to connect through cloud-based architectures in order to share information about their statu…
We present an Automatic Relevance Determination prior Bayesian Neural Network(BNN-ARD) weight l2-norm measure as a feature importance statistic for the model-x knockoff filter. We show on both simulated data and the Norwegian wind farm dataset that the proposed feature importance statistic yields statistically signific…
Wind energy forecasting helps to manage power production, and hence, reduces energy cost. Deep Neural Networks (DNN) mimics hierarchical learning in the human brain and thus possesses hierarchical, distributed, and multi-task learning capabilities. Based on aforementioned characteristics, we report Deep Belief Network …
Survey of yield farming protocols in DeFi.
Precision farming uses data analysis to optimize crop management.
Deep learning improves weather modeling for electricity load forecasting.
This study examines yield aggregators in DeFi, summarizing strategies and analyzing performance.
Condition monitoring is central to the efficient operation of wind farms due to the challenging operating conditions, rapid technology development and large number of aging wind turbines. In particular, predictive maintenance planning requires the early detection of faults with few false positives. Achieving this type …
Develops a method for probabilistic simulation of renewable energy production at grid scale.
Extended CSGE improves power and cyclist movement forecasting.
Deep learning enhances smart fish farming through automated feature extraction.
Bayesian neural networks speed up numerical integration.
Bayesian optimization adapted for experiments with changing environmental conditions.
In order to face the expected increasing demand of energy crops without creating conflicts of land occupation sustainability, farmers need to find reliable alternatives in marginal agricultural areas where the production of food hardly ever is economically and environmentally sustainable. The purpose of this work was t…
There is a small number of case studies of automatic land cover classification on the coastal area. Here, I test extraction of seagrass beds, sandy area, oyster farming rafts at Mangoku-ura Lagoon, Miyagi, Japan by comparing manual tracing, simple image segmentation, and image transformation using deep learning. The re…
Recent introduction of ICT in agriculture has brought a number of changes in the way farming is done. This means use of Internet of Things(IoT), Cloud Computing(CC), Big Data (BD) and automation to gain better control over the process of farming. As the use of these technologies in farms has grown exponentially with ma…
DeepMIDE forecasts wind speeds across space, time, and height for offshore wind energy.
Wind energy resource quantification, air pollution monitoring, and weather forecasting all rely on rapid, accurate measurement of local wind conditions. Visual observations of the effects of wind---the swaying of trees and flapping of flags, for example---encode information regarding local wind conditions that can pote…
An extreme wind speed estimation method that considers wind hazard climate types is critical for design wind load calculation for building structures affected by mixed climates. However, it is very difficult to obtain wind hazard climate types from meteorological data records, because they restrict the application of e…
Bayesian model transfers knowledge across different engineering fleets.
WindDragon forecasts wind power with deep learning.
Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed method uti…
New method calculates winding of geodesics on surfaces.
Survey of reinforcement learning for sustainable energy challenges.
Improved wind speed forecasts for power generation using machine learning.
DA improves solar wind forecasts by updating model boundary conditions.
Study predicts wind energy potential in Gulf of Oman using climate models.
We focus on wind power modeling using machine learning techniques. We show on real data provided by the wind energy company Ma{ï}a Eolis, that parametric models, even following closely the physical equation relating wind production to wind speed are outperformed by intelligent learning algorithms. In particular, the CA…
The paper defines price sensitivity and liquidity in CFMMs and links it to curvature.
Generative model improves wind field downscaling from coarse climate models.
Wind power prediction is of vital importance in wind power utilization. There have been a lot of researches based on the time series of the wind power or speed, but In fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction. In th…
A detailed understanding of wind turbine performance status classification can improve operations and maintenance in the wind energy industry. Due to different engineering properties of wind turbines, the standard supervised learning models used for classification do not generalize across data sets obtained from differ…