scholarly journals Review of Decreasing Wind Speed Extrapolation Error via Domain-Specific Feature Extraction and Selection

2019 ◽  
Author(s):  
Tuhfe Gocmen
Keyword(s):  
Feature Extraction ◽  
Wind Speed ◽  
Domain Specific ◽  
Feature Extraction And Selection ◽  
Extrapolation Error

scholarly journals Decreasing wind speed extrapolation error via domain-specific feature extraction and selection

2020 ◽  
Vol 5 (3) ◽  
pp. 959-975
Author(s):  
Daniel Vassallo ◽  
Raghavendra Krishnamurthy ◽  
Harindra J. S. Fernando
Keyword(s):  
Wind Speed ◽  
Power Law ◽  
Domain Knowledge ◽  
Test Case ◽  
Domain Specific ◽  
Promising Tool ◽  
Feature Extraction And Selection ◽  
Log Law ◽  
Wind Resources

Abstract. Model uncertainty is a significant challenge in the wind energy industry and can lead to mischaracterization of millions of dollars' worth of wind resources. Machine learning methods, notably deep artificial neural networks (ANNs), are capable of modeling turbulent and chaotic systems and offer a promising tool to produce high-accuracy wind speed forecasts and extrapolations. This paper uses data collected by profiling Doppler lidars over three field campaigns to investigate the efficacy of using ANNs for wind speed vertical extrapolation in a variety of terrains, and it quantifies the role of domain knowledge in ANN extrapolation accuracy. A series of 11 meteorological parameters (features) are used as ANN inputs, and the resulting output accuracy is compared with that of both standard log-law and power-law extrapolations. It is found that extracted nondimensional inputs, namely turbulence intensity, current wind speed, and previous wind speed, are the features that most reliably improve the ANN's accuracy, providing up to a 65 % and 52 % increase in extrapolation accuracy over log-law and power-law predictions, respectively. The volume of input data is also deemed important for achieving robust results. One test case is analyzed in depth using dimensional and nondimensional features, showing that the feature nondimensionalization drastically improves network accuracy and robustness for sparsely sampled atmospheric cases.

scholarly journals Decreasing Wind Speed Extrapolation Error via Domain-Specific Feature Extraction and Selection

2019 ◽  
Author(s):  
Daniel Vassallo ◽  
Raghavendra Krishnamurthy ◽  
Harindra J. S. Fernando
Keyword(s):  
Wind Speed ◽  
Power Law ◽  
Domain Knowledge ◽  
Test Case ◽  
Domain Specific ◽  
Promising Tool ◽  
Feature Extraction And Selection ◽  
Using Data ◽  
Meteorological Features ◽  
Wind Resource

Abstract. Model uncertainty is a significant challenge in the wind energy industry and can lead to mischaracterization of millions of dollars' worth of wind resource. Machine learning methods, notably deep artificial neural networks (ANNs), are capable of modeling turbulent and chaotic systems and offer a promising tool that can be used to produce high-accuracy wind speed forecasts and extrapolations. This paper quantifies the role of domain knowledge on ANN wind speed extrapolation accuracy using data collected using profiling lidars over three field campaigns. A series of 11 meteorological features are used as ANN inputs and the resulting output accuracy is compared with that of a simple power law extrapolation. It is found that normalized inputs, namely turbulence intensity, normalized current wind speed, and normalized previous wind speed, are the features that most reliably improve ANN accuracy, providing up to a 52 % increase in extrapolation accuracy over the power law predictions. The volume of input data is also deemed important for achieving robust results. One test case is analyzed in-depth using dimensional and non-dimensional features, showing that feature normalization drastically improves network accuracy and robustness for uncommon atmospheric cases.

Multi-step real-time wind speed prediction based on convolution memory network

2021 ◽  
pp. 0309524X2199826
Author(s):  
Guowei Cai ◽  
Yuqing Yang ◽  
Chao Pan ◽  
Dian Wang ◽  
Fengjiao Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wind Speed ◽  
Real Time ◽  
Wind Power ◽  
Spatial Correlation ◽  
Prediction Accuracy ◽  
Spatial Information ◽  
Spatial Association ◽  
Spatial Feature ◽  
Time Prediction

Multi-step real-time prediction based on the spatial correlation of wind speed is a research hotspot for large-scale wind power grid integration, and this paper proposes a multi-location multi-step wind speed combination prediction method based on the spatial correlation of wind speed. The correlation coefficients were determined by gray relational analysis for each turbine in the wind farm. Based on this, timing-control spatial association optimization is used for optimization and scheduling, obtaining spatial information on the typical turbine and its neighborhood information. This spatial information is reconstructed to improve the efficiency of spatial feature extraction. The reconstructed spatio-temporal information is input into a convolutional neural network with memory cells. Spatial feature extraction and multi-step real-time prediction are carried out, avoiding the problem of missing information affecting prediction accuracy. The method is innovative in terms of both efficiency and accuracy, and the prediction accuracy and generalization ability of the proposed method is verified by predicting wind speed and wind power for different wind farms.

scholarly journals Penerapan K-Means Clustering Untuk Seleksi Frame Dominan Berbasis NTSC Pada Obyek Bergerak

2020 ◽  
Vol 7 (4) ◽  
pp. 745
Author(s):  
Rizka Indah Armianti ◽  
Achmad Fanany Onnilita Gaffar ◽  
Arief Bramanto Wicaksono Putra
Keyword(s):  
Feature Extraction ◽  
Video Data ◽  
Clustering Method ◽  
Data Frame ◽  
First Order ◽  
Feature Extraction And Selection ◽  
Different Shapes ◽  
Frame Set ◽  
Selection Of

<p class="Abstrak">Obyek dinyatakan bergerak jika terjadi perubahan posisi dimensi disetiap <em>frame</em>. Pergerakan obyek menyebabkan obyek memiliki perbedaan bentuk pola disetiap <em>frame-</em>nya. <em>Frame</em> yang memiliki pola terbaik diantara <em>frame</em> lainnya disebut <em>frame</em> dominan. Penelitian ini bertujuan untuk menyeleksi <em>frame</em> dominan dari rangkaian <em>frame</em> dengan menerapkan metode K-means <em>clustering</em> untuk memperoleh <em>centroid</em> dominan (<em>centroid</em> dengan nilai tertinggi) yang digunakan sebagai dasar seleksi <em>frame</em> dominan. Dalam menyeleksi <em>frame</em> dominan terdapat 4 tahapan utama yaitu akuisisi data, penetapan pola obyek, ekstrasi ciri dan seleksi. Data yang digunakan berupa data video yang kemudian dilakukan proses penetapan pola obyek menggunakan operasi pengolahan citra digital, dengan hasil proses berupa pola obyek RGB yang kemudian dilakukan ekstraksi ciri berbasis NTSC dengan menggunakan metode statistik orde pertama yaitu <em>Mean</em>. Data hasil ekstraksi ciri berjumlah 93 data <em>frame</em> yang selanjutnya dikelompokkan menjadi 3 <em>cluster</em> menggunakan metode K-Means. Dari hasil <em>clustering</em>, <em>centroid</em> dominan terletak pada <em>cluster</em> 3 dengan nilai <em>centroid</em> 0.0177 dan terdiri dari 41 data <em>frame</em>. Selanjutnya diukur jarak kedekatan seluruh data <em>cluster</em> 3 terhadap <em>centroid</em>, data yang memiliki jarak terdekat dengan <em>centroid</em> itulah <em>frame</em> dominan. Hasil seleksi <em>frame</em> dominan ditunjukkan pada jarak antar <em>centroid</em> dengan anggota <em>cluster</em>, dimana dari seluruh 41 data frame tiga jarak terbaik diperoleh adalah 0.0008 dan dua jarak bernilai  0.0010 yang dimiliki oleh <em>frame</em> ke-59, ke-36 dan ke-35.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The object is declared moving if there is a change in the position of the dimensions in each frame. The movement of an object causes the object to have different shapes in each frame. The frame that has the best pattern among other frames is called the dominant frame. This study aims to select the dominant frame from the frame set by applying the K-means clustering method to obtain the dominant centroid (the highest value centroid) which is used as the basis for the selection of dominant frames. In selecting dominant frames, there are 4 main stages, namely data acquisition, determination of object patterns, feature extraction and selection. The data used in the form of video data which is then carried out the process of determining the pattern of objects using digital image processing operations, with the results of the process in the form of an RGB object pattern which is then performed NTSC-based feature extraction using the first-order statistical method, Mean. The data from feature extraction are 93 data frames which are then grouped into 3 clusters using the K-Means method. From the results of clustering, the dominant centroid is located in cluster 3 with a centroid value of 0.0177 and consists of 41 data frames. Furthermore, the proximity of all data cluster 3 to the centroid is measured, the data having the closest distance to the centroid is the dominant frame. The results of dominant frame selection are shown in the distance between centroids and cluster members, where from all 41 data frames the three best distances obtained are 0.0008, 0.0010, and 0.0010 owned by 59th, 36th and 35th frames.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p><p> </p>

scholarly journals VESSEL CLASSIFICATION IN COSMO-SKYMED SAR DATA USING HIERARCHICAL FEATURE SELECTION

2015 ◽  
Vol XL-7/W3 ◽  
pp. 975-982 ◽  
Author(s):  
A. Makedonas ◽  
C. Theoharatos ◽  
V. Tsagaris ◽  
V. Anastasopoulos ◽  
S. Costicoglou
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Selection Procedure ◽  
Texture Features ◽  
Statistical Characteristics ◽  
Selection Scheme ◽  
Feature Extraction And Selection ◽  
Sar Data ◽  
Ship Classification ◽  
Analysis Scale

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features’ statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

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