A two-stage framework for short-term wind power forecasting using different feature-learning models

With the growing dependence on wind power generation, improving the accuracy of short-term forecasting has become increasingly important for ensuring continued economical and reliable system operations. In the wind power forecasting field, ensemble-based forecasting models have been studied extensively; however, few of them considered learning the features from both historical wind data and NWP data. In addition, the exploration of the multiple-input and multiple-output learning structures is lacking in the wind power forecasting literature. Therefore, this study exploits the NWP and historical wind data as input and proposes a two-stage forecasting framework on the shelf of moving window algorithm. Specifically, at the first stage, four forecasting models are constructed with deep neural networks considering the multiple-input and multiple-output structures; at the second stage, an ensemble model is developed using ridge regression method for reducing the extrapolation error. The experiments are conducted on three existing wind farms for examining the 2-h ahead forecasting point. The results demonstrate that 1) the single-input-multiple-output (SIMO) structure leads to a better forecasting accuracy than the other threes; 2) ridge regression method results in a better ensemble model that is able to further improve the forecasting accuracy, than the other machine learning methods; 3) the proposed two-stage forecasting framework is likely to generate more accurate and stable results than the other existing algorithms

The Emerging Trends of Multi-Label Learning

Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with an extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there has been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfill this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202

Data Driven Approach To Saltwater Disposal (SWD) Well Location Optimization In North Dakota

The sharp increase in oil and gas production in the Williston Basin of North Dakota since 2006 has resulted in a significant increase in produced water volumes. Primary mechanism for disposal of produced water is by injection into underground Inyan Kara formation through Class-II Saltwater Disposal (SWD) wells. With number of SWD wells anticipated to increase from 900 to over 1400 by 2035, localized pressurization and other potential issues that could affect performance of future oil and SWD wells, there was a need for a reliable model to select locations of future SWD wells for optimum performance. Since it is uncommon to develop traditional geological and simulation models for SWD wells, this research focused on developing data-driven proxy models based on the CRISP-Data Mining pipeline for understanding SWD well performance and optimizing future well locations. NDIC’s oil and gas division was identified as the primary data source. Significant efforts went towards identifying other secondary data sources, extracting required data from primary and secondary data sources using web scraping, integrating different data types including spatial data and creating the final data set. Orange visual programming application and Python programming language were used to carry out the required data mining activities. Exploratory Data Analysis and clustering analysis were used to gain a good understanding of the features in the data set and their relationships. Graph Data Science techniques such as Knowledge Graphs and graph-based clustering were used to gain further insights. Machine Learning regression algorithms such as Multi-Linear Regression, k-Nearest Neighbors and Random Forest were used to train machine learning models to predict average monthly barrels of saltwater disposed in a well. Model performance was optimized using the RMSE metric and the Random Forest model was selected as the final model for deployment to predict performance of a planned SWD well. A multi-target regression model was trained using deep neural network to predict water production in oil and gas wells drilled in the McKenzie county of North Dakota

Інтелектуальна система розпізнавання обігових монет

Розмір пояснювальної записки – 134 аркушів, містить 53 ілюстрації, 32 таблиць, 10 додатків. У магістерській дисертації розглянуто проблему розпізнавання образів в області розпізнавання зображень обігових монет, показано основні особливості існуючих програмних рішень, розглянуто їх переваги та недоліки. Об’єктом дослідження в даній дисертації виступає процес розпізнавання монет за їх зображеннями. Предметом дослідження даної дисертації є алгоритми та способи, за допомогою яких можна виконувати розпізнавання обігових монет за зображеннями двох сторін монети. Розроблено інтелектуальну систему розпізнавання обігових монет, яка заснована на згортковій нейронній мережі з декількома виходами. У роботі виконано аналіз існуючих підходів та алгоритмів, які використовуються для розпізнавання зображень. Надано опис архітектури спроєктованої штучної нейронної мережі, набору даних та процесу навчання мережі. Для реалізації доступу до можливостей системи було розроблено веб-додаток. Дана система може бути використана користувачами з різним рівнем зацікавленості у нумізматиці для розпізнавання обігових монет і дозволяє суттєво зменшити час на визначення характеристик шуканої монети.Explanatory note size – 134 pages, contains 53 illustrations, 32 tables, 10 applications. Examines the problem of pattern recognition in the area of circulation coins image recognition, shows the main features of existing software solutions, considers their advantages and disadvantages. The object of research in this dissertation is the process of coin recognition by coin images. The subject of research in this dissertation are the algorithms and methods with which you can perform the circulating coins recognition using the images of the two sides of the coin. An intelligent system for circulation coins recognition, which is based on a convolutional neural network with several outputs was developed. The paper analyzes the existing approaches and algorithms used for image recognition. A description of the architecture of the designed artificial neural network, data set and network learning process were given. A web application was developed to implement access to the system’s capabilities. This system can be used by users with different levels of interest in numismatics to recognize circulation coins and allows to significantly reduce the time to determine the characteristics of the required coin