Use of Machine Learning Methods for Gas Export Volume Predictions

2021 ◽  
Author(s):  
Abdulmalik Ibragimov
Keyword(s):  
Machine Learning ◽  
Ambient Temperature ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Sales Volume ◽  
Climate Extreme ◽  
Hydrocarbon Production ◽  
Export Volume ◽  
Negative Impacts

Abstract Gas sales volume have a significant impact on oil production in the Karachagank field due to gas injection constraints. Ambient temperature is one of the variables influencing gas sales volume. As global warming takes a toll on a climate, extreme weather conditions become frequent in the region hindering hydrocarbon production. The author used machine-learning techniques to predict gas sales volume based on weather forecast data. The results of prediction allow foreseeing potential possible drops in export volumes that can help field staff in proactive planning for opportunity maintenance on wells, the surface network, and a gas plant thus helping in avoiding large negative impacts caused by high ambient temperature.

scholarly journals Blue Skies: A Methodology for Data-Driven Clear Sky Modelling

2017 ◽  
Author(s):  
Kartik Palani ◽  
Ramachandra Kota ◽  
Amar Prakash Azad ◽  
Vijay Arya
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Weather Conditions ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Clear Sky ◽  
Learning Techniques ◽  
Atmospheric Data ◽  
Average Irradiance ◽  
Photo Voltaic

One of the major challenges confronting the widespread adoption of solar energy is the uncertainty of production. The energy generated by photo-voltaic systems is a function of the received solar irradiance which varies due to atmospheric and weather conditions. A key component required for forecasting irradiance accurately is the clear sky model which estimates the average irradiance at a location at a given time in the absence of clouds. Current methods for modelling clear sky irradiance are either inaccurate or require extensive atmospheric data, which tends to vary with location and is often unavailable. In this paper, we present a data-driven methodology, Blue Skies, for modelling clear sky irradiance solely based on historical irradiance measurements. Using machine learning techniques, Blue Skies is able to generate clear sky models that are more accurate spatio-temporally compared to the state of the art, reducing errors by almost 50%.

scholarly journals Automated predictive analytics tool for rainfall forecasting

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maulin Raval ◽  
Pavithra Sivashanmugam ◽  
Vu Pham ◽  
Hardik Gohel ◽  
Ajeet Kaushik ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Weather Conditions ◽  
Machine Learning Algorithms ◽  
Rainfall Data ◽  
Machine Learning Techniques ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Prediction Techniques

AbstractAustralia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.

scholarly journals Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2570
Author(s):  
Christil Pasion ◽  
Torrey Wagner ◽  
Clay Koschnick ◽  
Steven Schuldt ◽  
Jada Williams ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Ambient Temperature ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Solar Panels ◽  
Power Prediction ◽  
Location Data ◽  
Learning Techniques ◽  
Prior Literature

Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined dataset with an R2 value of 0.94. As a comparative measure, other machine learning algorithms resulted in R2 values of 0.50–0.94. Additionally, the data from each location was modeled separately with R2 values ranging from 0.91 to 0.97, indicating a range of consistency across all sites. Using an input variable permutation approach with the random forest algorithm, we found that the three most important variables for power prediction were ambient temperature, humidity, and cloud ceiling. The analysis showed that machine learning potentially allowed for accurate power prediction while avoiding the challenges associated with modeled irradiation data.

scholarly journals MACHINE LEARNING FOR SEA ICE MONITORING FROM SATELLITES

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 83-89
Author(s):  
C. O. Dumitru ◽  
V. Andrei ◽  
G. Schwarz ◽  
M. Datcu
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Weather Conditions ◽  
Representation Learning ◽  
Image Understanding ◽  
Machine Learning Techniques ◽  
Polar Regions ◽  
Learning Approaches ◽  
Automated Processing ◽  
Modern Machine

<p><strong>Abstract.</strong> Today, radar imaging from space allows continuous and wide-area sea ice monitoring under nearly all weather conditions. To this end, we applied modern machine learning techniques to produce ice-describing semantic maps of the polar regions of the Earth. Time series of these maps can then be exploited for local and regional change maps of selected areas. What we expect, however, are fully-automated unsupervised routine classifications of sea ice regions that are needed for the rapid and reliable monitoring of shipping routes, drifting and disintegrating icebergs, snowfall and melting on ice, and other dynamic climate change indicators. Therefore, we designed and implemented an automated processing chain that analyses and interprets the specific ice-related content of high-resolution synthetic aperture radar (SAR) images. We trained this system with selected images covering various use cases allowing us to interpret these images with modern machine learning approaches. In the following, we describe a system comprising representation learning, variational inference, and auto-encoders. Test runs have already demonstrated its usefulness and stability that can pave the way towards future artificial intelligence systems extending, for instance, the current capabilities of traditional image analysis by including content-related image understanding.</p>

Nowcasting Lightning Occurrence Using Machine Learning Techniques: The Challenge of Identifying Outliers

2020 ◽  
Author(s):  
Amirhossein Mostajabi ◽  
Declan Finney ◽  
Marcos Rubinstein ◽  
Farhad Rachidi
Keyword(s):  
Machine Learning ◽  
Forest Fires ◽  
Meteorological Parameters ◽  
Warning System ◽  
Weather Forecast ◽  
Field Analysis ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Learning Techniques ◽  
Kennedy Space

&lt;p&gt;Lightning is formed in the atmosphere through the combination of complex dynamic and microphysical processes. Lightning can have a considerable influence on the environment and on the economy since it causes energy supply outages, forest fires, damages, injury and death of humans and livestock worldwide. Therefore, it is of great importance to be able to predict lightning incidence in order to protect people and installations. Despite numerous attempts to solve the important problem of lightning prediction (e.g., [1]&amp;#8211;[3]), the complex processes and large number of parameters involved in the problem lend themselves to the potential application of a machine learning (ML) approach.&lt;/p&gt;&lt;p&gt;We recently proposed a ML-based lightning early-warning system with promising performance [4]. The proposed ML model is trained to nowcast lightning incidence during any one of&amp;#160; three consecutive 10-minute time intervals and within a circular area of 30 km radius around a meteorological station. The system uses the real-time measured values of four meteorological parameters that are relevant to the mechanisms of electric charge generation in thunderstorms, namely the air pressure at station level (QFE), the air temperature 2 m above ground, the relative humidity, and the wind speed. The proposed algorithm was implemented using the data from 12 meteorological stations in Switzerland between 2006-2017 with a granularity of ten minutes. The stations were selected to be well distributed among different ranges of altitude and terrain topographies.&lt;/p&gt;&lt;p&gt;The algorithm requires the filtering out of a portion of the data which are identified as outliers. However, the process of the automatic identification of outliers is a challenging task which could also affect the model&amp;#8217;s performance. In this presentation, we discuss this problem and present approaches that can be used to optimize the process.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;[1]&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; D. Aranguren, J. Montanya, G. Sol&amp;#225;, V. March, D. Romero, and H. Torres, &amp;#8220;On the lightning hazard warning using electrostatic field: Analysis of summer thunderstorms in Spain,&amp;#8221; J. Electrostat., vol. 67, no. 2&amp;#8211;3, pp. 507&amp;#8211;512, May 2009.&lt;/p&gt;&lt;p&gt;[2]&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; G. N. Seroka, R. E. Orville, and C. Schumacher, &amp;#8220;Radar Nowcasting of Total Lightning over the Kennedy Space Center,&amp;#8221; Weather Forecast., vol. 27, no. 1, pp. 189&amp;#8211;204, Feb. 2012.&lt;/p&gt;&lt;p&gt;[3]&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; Q. Meng, W. Yao, and L. Xu, &amp;#8220;Development of Lightning Nowcasting and Warning Technique and Its Application,&amp;#8221; Adv. Meteorol., vol. 2019, pp. 1&amp;#8211;9, Jan. 2019.&lt;/p&gt;&lt;p&gt;[4]&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; A. Mostajabi, D. L. Finney, M. Rubinstein, and F. Rachidi, &amp;#8220;Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques,&amp;#8221; npj Clim. Atmos. Sci., vol. 2, no. 1, p. 41, 2019.&lt;/p&gt;

scholarly journals Machine Learning Approach

2022 ◽  
pp. 349-366
Author(s):  
Roopashree S. ◽  
Anitha J. ◽  
Madhumathy P.
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Medicinal Herbs ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Domain Experts ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Insufficient Knowledge ◽  
Complete Framework

Ayurveda medicines uses herbs for curing many ailments without side effects. The biggest concern related to Ayurveda medicine is extinction of many important medicinal herbs, which may be due to insufficient knowledge, weather conditions, and urbanization. Another reason consists of lack of online facts on Indian herbs because it is dependent on books and experts. This concern has motivated in utilizing the machine learning techniques to identify and reveal few details of Indian medicinal herbs because, until now, it is identified manually, which is cumbersome and may lead to errors. Many researchers have shown decent results in identifying and classifying plants with good accuracy and robustness. But no complete framework and strong evidence is projected on Indian medicinal herbs. Accordingly, the chapter aims to provide an outline on how machine learning techniques can be adopted to enrich the knowledge of Indian herbs, which advantages both common man and the domain experts with wide information on traditional herbs.

scholarly journals Machine Learning Approach

2021 ◽  
pp. 214-231
Author(s):  
Roopashree S. ◽  
Anitha J. ◽  
Madhumathy P.
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Medicinal Herbs ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Domain Experts ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Insufficient Knowledge ◽  
Complete Framework

Ayurveda medicines uses herbs for curing many ailments without side effects. The biggest concern related to Ayurveda medicine is extinction of many important medicinal herbs, which may be due to insufficient knowledge, weather conditions, and urbanization. Another reason consists of lack of online facts on Indian herbs because it is dependent on books and experts. This concern has motivated in utilizing the machine learning techniques to identify and reveal few details of Indian medicinal herbs because, until now, it is identified manually, which is cumbersome and may lead to errors. Many researchers have shown decent results in identifying and classifying plants with good accuracy and robustness. But no complete framework and strong evidence is projected on Indian medicinal herbs. Accordingly, the chapter aims to provide an outline on how machine learning techniques can be adopted to enrich the knowledge of Indian herbs, which advantages both common man and the domain experts with wide information on traditional herbs.

Procedure for combining field measurements and machine learning to quantify impact of different track parameters on ride quality of railway tracks

2021 ◽  
pp. 095440972110026
Author(s):  
Alireza Roghani ◽  
Raman Pall ◽  
Elton Toma
Keyword(s):  
Machine Learning ◽  
Statistical Model ◽  
Field Measurements ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Ride Quality ◽  
Fundamental Factor ◽  
Learning Techniques ◽  
Train Speed ◽  
The Impact

Ride quality in terms of vibration is a fundamental factor affecting passengers’ satisfaction. Every year, passenger carriers invest significantly in various aspects of their system, including track and infrastructure, to improve ride quality. The assessment of ride quality and understanding the extent of the impact of different parameters on its magnitude is essential for railway operators to make informed decisions regarding capital expenditures. This paper presents a methodology for using machine learning techniques to find the correlation between various parameters (including train speed, weather conditions, presence of track features, and composition of the track substructure) and ride quality (quantified using measurements from accelerometers mounted on a rail car). The statistical model was developed using field measurements collected over a 50 km section of VIA Rail’s track in Canada. This paper describes the collected field data, the development of the statistical model, and discusses the importance of each parameter on the accuracy of the model.

Review on Different Traditional and Machine Learning Techniques for Irrigation Planning for Crop Yield Prediction

2020 ◽  
Vol 17 (9) ◽  
pp. 3831-3838
Author(s):  
K. M. Sowmya Shree ◽  
M. N. Veena
Keyword(s):  
Machine Learning ◽  
Crop Yields ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Irrigation Planning ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Productivity Rate ◽  
Major Factors ◽  
Variable Analysis

Agriculture is one of the major factors of Indian economy which involves production of crops. Production crops may be food crops or commercial crops like wheat, maize, grams, rice, millets, cotton etc. The productivity of the crops is administered by its weather conditions. Forecasting the crop yields is a challenging task which needs to be addressed. Several data mining technologies are explored for forecasting the crop yields, yet, solutions are complex and infeasible. This paper presents a review of machine learning techniques for irrigation planning to forecast the crop yields are discussed. Various machine learning methods like prediction, classification, regression, clustering are discussed. This study brings a need for an enhancement in irrigation planning using machine learning techniques. To increase the productivity rate of the crops, variable analysis also play a significant part in defining predictive models. Comparative analysis is done on machine learning techniques and its benefits are explored.

Machine learning applied to pack classification

2019 ◽  
Vol 61 (6) ◽  
pp. 601-620
Author(s):  
Weiqiang Hang ◽  
Timothy Banks
Keyword(s):  
Machine Learning ◽  
Market Research ◽  
Hierarchical Classification ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Sales Volume ◽  
Related Services ◽  
Learning Techniques ◽  
Common Task ◽  
Product Classification

Pack or product classification is a quite common task in market research, particularly for sales tracking audits and related services. Electronic data sources have led to increased volumes, both in the sales volume being tracked and also the number of packs (or stock keeping units). The increase in packs needing to be classified presents a problem, in that, it needs to be done accurately and quickly. Traditional solutions using people for the classifications can be costly, due to the large number of people required to process the classifications in a timely and accurate manner. Reducing the manual work is a priority for audit-based market research businesses, leading to interest in automation, such as through machine learning techniques. In this article, we apply such methods. These include support vector machine, decision tree, XGBoost, AdaBoost, random forest, and neural network–based methods that are trained on the textual descriptions of already classified packs. We also implement a hierarchical classification method to take advantage of the structure of classes of the products. Once the models are trained, they can be used on unclassified data. Where the methods are not confident in their classifications, humans can be asked to classify. The hope is that the methods can learn to classify accurately enough that the manual workloads are reduced to manageable levels. This article reviews various methods and then outlines tests using these methods on two datasets collected by Nielsen, showing good performance.

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