Machine Learning Combination of LEO and GEO Satellites for Design and Monitoring of Ocean Wind Energy

2021 ◽  
Author(s):  
Christophe Messager ◽  
Tran Vu La ◽  
Sahl Remi
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Geo Satellites ◽  
Ocean Wind

scholarly journals Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 338
Author(s):  
Lorenzo Donadio ◽  
Jiannong Fang ◽  
Fernando Porté-Agel
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Power Forecasting ◽  
Numerical Weather ◽  
Artificial Neural ◽  
Power Forecasting

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

scholarly journals Improving Lidar-Derived Turbulence Estimates for Wind Energy

2016 ◽  
Author(s):  
Jennifer F. Newman ◽  
Andrew Clifton
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Wind Energy ◽  
Sonic Anemometer ◽  
Turbulence Models ◽  
Volume Averaging ◽  
Turbine Rotor ◽  
Error Reduction ◽  
Power Prediction ◽  
Wind Speeds

Abstract. Remote sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, commercially available lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The algorithm, L-TERRA, can be applied using only data from a stand-alone commercially available lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. L-TERRA was tested on data from three sites – two in flat terrain and one in semicomplex terrain. L-TERRA significantly reduced errors in lidar turbulence at all three sites, even when the machine-learning portion of the model was trained on one site and applied to a different site. Errors in turbulence were then related to errors in power through the use of a power prediction model for a simulated 1.5 MW turbine. L-TERRA also reduced errors in power significantly at all three sites, although moderate power errors remained for periods when the mean wind speed was close to the rated wind speed of the turbine and periods when variance contamination had a large effect on the lidar turbulence error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

scholarly journals A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5196
Author(s):  
Upma Singh ◽  
Mohammad Rizwan ◽  
Muhannad Alaraj ◽  
Ibrahim Alsaidan
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Robust Regression ◽  
Energy Generation ◽  
Sustainable Growth ◽  
Gradient Boosting ◽  
Wind Energy Generation ◽  
Gradient Boosting Machine

In the last few years, several countries have accomplished their determined renewable energy targets to achieve their future energy requirements with the foremost aim to encourage sustainable growth with reduced emissions, mainly through the implementation of wind and solar energy. In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree, and extra tree regression, which are applied to improve the forecasting accuracy of short-term wind energy generation in the Turkish wind farms, situated in the west of Turkey, on the basis of a historic data of the wind speed and direction. Polar diagrams are plotted and the impacts of input variables such as the wind speed and direction on the wind energy generation are examined. Scatter curves depicting relationships between the wind speed and the produced turbine power are plotted for all of the methods and the predicted average wind power is compared with the real average power from the turbine with the help of the plotted error curves. The results demonstrate the superior forecasting performance of the algorithm incorporating gradient boosting machine regression.

A Low Specific Mass, Free Floating Wind Energy Concept up to 40 MW

2019 ◽  
Author(s):  
William Alexander
Keyword(s):  
Wind Energy ◽  
High Stability ◽  
Open Ocean ◽  
Liquid Fuels ◽  
Power Capacity ◽  
Specific Mass ◽  
Energy Concept ◽  
Wave Heights ◽  
Ocean Wind ◽  
Structural Mass

Abstract Presented here is a low specific mass, free-floating, open ocean, wind energy concept with nominal power capacity to 40 MW, on-board liquid fuels generation, and with operational and survival wave heights to 12 and 40 meters respectively. The estimated specific structural mass of 42 kG/kWp is about 1/3 of the specific mass of much smaller land-based turbines, and less than 6% of the specific structural mass of existing off-shore floating wind turbines. The turbine platform may be operated un-tethered in the open ocean using about 8% of the generated power, on average, for active station keeping. The generated energy may be stored on board via hydrogen electrolysis and liquification for periodic tanker unloading. Reduction of moment loads in the blades and nacelle support structure as well as the unique deep-water foundation result in the low specific mass and high stability.

scholarly journals Multitask Support Vector Regression for Solar and Wind Energy Prediction

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6308
Author(s):  
Carlos Ruiz ◽  
Carlos M. Alaíz ◽  
José R. Dorronsoro
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Support Vector Regression ◽  
Energy Production ◽  
Multitask Learning ◽  
Support Vector ◽  
Prediction Problem ◽  
Energy Prediction ◽  
The Common ◽  
The Impact

Given the impact of renewable sources in the overall energy production, accurate predictions are becoming essential, with machine learning becoming a very important tool in this context. In many situations, the prediction problem can be divided into several tasks, more or less related between them but each with its own particularities. Multitask learning (MTL) aims to exploit this structure, training several models at the same time to improve on the results achievable either by a common model or by task-specific models. In this paper, we show how an MTL approach based on support vector regression can be applied to the prediction of photovoltaic and wind energy, problems where tasks can be defined according to different criteria. As shown experimentally with three different datasets, the MTL approach clearly outperforms the results of the common and specific models for photovoltaic energy, and are at the very least quite competitive for wind energy.

scholarly journals A Dual-Step Integrated Machine Learning Model for 24h-Ahead Wind Energy Generation Prediction Based on Actual Measurement Data and Environmental Factors

2019 ◽  
Vol 9 (10) ◽  
pp. 2125 ◽  
Author(s):  
Yuan-Jia Ma ◽  
Ming-Yue Zhai
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Environmental Factors ◽  
Wind Energy ◽  
Wind Power ◽  
Forecast Accuracy ◽  
Energy Generation ◽  
Power Grids ◽  
Second Step ◽  
Wind Energy Generation

Wind power generation output is highly uncertain, since it entirely depends on intermittent environmental factors. This has brought a serious problem to the power industry regarding the management of power grids containing a significant penetration of wind power. Therefore, a highly accurate wind power forecast is very useful for operating these power grids effectively and sustainably. In this study, a new dual-step integrated machine learning (ML) model based on the hybridization of wavelet transform (WT), ant colony optimization algorithm (ACO), and feedforward artificial neural network (FFANN) is devised for a 24 h-ahead wind energy generation forecast. The devised model consists of dual steps. The first step uses environmental factors (weather variables) to estimate wind speed at the installation point of the wind generation system. The second step fits the wind farm actual generation with the actual wind speed observation at the location of the farm. The predicted future speed in the first step is later given to the second step to estimate the future generation of the farm. The devised method achieves significantly acceptable and promising forecast accuracy. The forecast accuracy of the devised method is evaluated through several criteria and compared with other ML based models and persistence based reference models. The daily mean absolute percentage error (MAPE), the normalized mean absolute error (NMAE), and the forecast skill (FS) values achieved by the devised method are 4.67%, 0.82%, and 56.22%, respectively. The devised model outperforms all the evaluated models with respect to various performance criteria.

Prognostics and Health Management of Wind Energy Infrastructure Systems

2022 ◽  
Author(s):  
Celalettin Yuce ◽  
Ozhan Gecgel ◽  
Oguz Dogan ◽  
Shweta Dabetwar ◽  
Yasar Yanik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Data Augmentation ◽  
Health Management ◽  
Prognostics And Health Management ◽  
Quality Of Data ◽  
Energy Infrastructure ◽  
Infrastructure Systems ◽  
The Impact

Abstract The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute towards the Prognostics and Health Management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.

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