Author: Saud Al-Fattah Publisher: ISBN: Category : Languages : en Pages : 6
Book Description
This paper develops a rigorous and advanced computational model to describe, analyze, and forecast global crude oil demand. The paper deploys a hybrid approach of artificial intelligence techniques: artificial neural network and genetic algorithms, to devise a methodological framework for developing forecasting models of global crude oil demand. We piloted two country cases of a high oil producer (Saudi Arabia) and a high oil consumer (China) to illustrate the effectiveness and applicability of the proposed methodology for developing oil demand outlook using artificial intelligence.The input variables of the neural network models include gross domestic product, population, oil prices, gas prices, and transport data, in addition to transformed variables and functional links. The artificial intelligent predictive models of oil demand were successfully developed, trained, validated and tested using historical oil-market data yielding excellent predictions of oil demand. The performance of the intelligent models of Saudi Arabia and China were examined for generalization attribute, predictability, and accuracy. Oil demand models for Saudi Arabia and China achieved a high prediction accuracy of a correlation coefficient of 0.975 and 0.996, respectively.The intelligent outlook models show that crude oil demand for both Saudi Arabia and China will continue to increase for the outlook period (2018-2022) but with mildly declining growth. This falling growth of oil demand can be attributed to the increase in energy efficiency, fuel switching, conversion of power plants from crude to gas-based plants, and an increase in the utilization of renewable energy such as solar and wind for electric generation and water desalination.The methodology proposed improves and enhances the conventional process of developing the oil demand outlook. It also improves and enhances the predictability and accuracy of current forecasting models of oil demand. In this study, features selection techniques are applied to identify and understand the endogenous and exogenous factors that influence global energy markets, particularly those factors that impact and drive global oil demand.
Author: Kingshuk Srivastava Publisher: CRC Press ISBN: 1000995119 Category : Technology & Engineering Languages : en Pages : 187
Book Description
This book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies. Features: Provides an understanding of the basics of IT technologies applied in the oil and gas sector Includes deep comparison between different artificial intelligence techniques Analyzes different simulators in the oil and gas sector as well as discussion of AI applications Focuses on in-depth experimental and applied topics Details different case studies for upstream and downstream This book is aimed at professionals and graduate students in petroleum engineering, upstream industry, data analytics, and digital transformation process in oil and gas.
Author: Mohsen Hamoudia Publisher: Springer Nature ISBN: 3031358791 Category : Business & Economics Languages : en Pages : 441
Book Description
This book is a comprehensive guide that explores the intersection of artificial intelligence and forecasting, providing the latest insights and trends in this rapidly evolving field. The book contains fourteen chapters covering a wide range of topics, including the concept of AI, its impact on economic decision-making, traditional and machine learning-based forecasting methods, challenges in demand forecasting, global forecasting models, meta-learning and feature-based forecasting, ensembling, deep learning, scalability in industrial and optimization applications, and forecasting performance evaluation. With key illustrations, state-of-the-art implementations, best practices, and notable advances, this book offers practical insights into the theory and practice of AI-based forecasting. This book is a valuable resource for anyone involved in forecasting, including forecasters, statisticians, data scientists, business analysts, or decision-makers.
Author: Yoshua Bengio Publisher: Now Publishers Inc ISBN: 1601982941 Category : Computational learning theory Languages : en Pages : 145
Book Description
Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
Author: Abdolhossein Hemmati-Sarapardeh Publisher: Gulf Professional Publishing ISBN: 0128223855 Category : Science Languages : en Pages : 324
Book Description
Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges. - Teaches how to apply machine learning algorithms that work best in exploration, drilling, reservoir or production engineering - Helps readers increase their existing knowledge on intelligent data modeling, machine learning and artificial intelligence, with foundational chapters covering the preprocessing of data and training on algorithms - Provides tactics on how to cover complex projects such as shale gas, tight oils, and other types of unconventional reservoirs with more advanced model input
Author: Saud Al-Fattah Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Energy market volatility affects macroeconomic conditions and can unduly affect the economies of energy-producing countries. Large price swings can be detrimental to both producers and consumers. Market volatility can cause infrastructure and capacity investments to be delayed, employment losses, and inefficient investments. In sum, the growth potential for energy-producing countries is adversely affected. Undoubtedly, greater stability of oil prices can reduce uncertainty in energy markets, for the benefit of consumers and producers alike. Therefore, modeling and forecasting crude oil price volatility is critical in many financial and investment applications. The purpose of this paper to develop new predictive models for describing and forecasting the global oil price volatility using artificial intelligence with artificial neural network (ANN) modeling technology. Applying the novel approach of ANN, two models were successfully developed: one for WTI futures price volatility and the other for WTI spot prices volatility. These models were successfully designed, trained, verified, and tested using historical oil market data. The estimations and predictions from the ANN models closely match the historical data of WTI from January 1994 to April 2012. They appear to capture very well the dynamics and the direction of the oil price volatility. These ANN models developed in this study can be used: as short-term as well as long-term predictive tools for the direction of oil price volatility, to quantitatively examine the effects of various physical and economic factors on future oil market volatility, to understand the effects of different mechanisms for reducing market volatility, and to recommend policy options and programs incorporating mechanisms that can potentially reduce the market volatility. With this improved method for modeling oil price volatility, experts and market analysts will be able to empirically test new approaches to mitigating market volatility. The outcome of this work provides a roadmap for research to improve predictability and accuracy of energy and crude models.
Author: Michael Negnevitsky Publisher: Pearson Education ISBN: 9780321204660 Category : Computers Languages : en Pages : 454
Book Description
Keeping the maths to a minimum, Negnevitsky explains the principles of AI, demonstrates how systems are built, what they are useful for and how to choose the right tool for the job.
Author: Kingshuk Srivastava Publisher: ISBN: 9781003357872 Category : Gas industry Languages : en Pages : 0
Book Description
This book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies. Features: Provides an understanding of the basics of IT technologies applied in the oil and gas sector Includes deep comparison between different artificial intelligence techniques Analyzes different simulators in the oil and gas sector as well as discussion of AI applications Focuses on in-depth experimental and applied topics Details different case studies for upstream and downstream This book is aimed at professionals and graduate students in petroleum engineering, upstream industry, data analytics, and digital transformation process in oil and gas.
Author: Vivienne Sze Publisher: Springer Nature ISBN: 3031017668 Category : Technology & Engineering Languages : en Pages : 254
Book Description
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Author: Ashutosh Kumar Dubey Publisher: Woodhead Publishing ISBN: 0323906613 Category : Science Languages : en Pages : 408
Book Description
Artificial Intelligence for Renewable Energy Systems addresses the energy industries remarkable move from traditional power generation to a cost-effective renewable energy system, and most importantly, the paradigm shift from a market-based cost of the commodity to market-based technological advancements. Featuring recent developments and state-of-the-art applications of artificial intelligence in renewable energy systems design, the book emphasizes how AI supports effective prediction for energy generation, electric grid related line loss prediction, load forecasting, and for predicting equipment failure prevention. Looking at approaches in system modeling and performance prediction of renewable energy systems, this volume covers power generation systems, building service systems and combustion processes, exploring advances in machine learning, artificial neural networks, fuzzy logic, genetic algorithms and hybrid mechanisms. - Includes real-time applications that illustrates artificial intelligence and machine learning for various renewable systems - Features a templated approach that can be used to explore results, with scientific implications followed by detailed case studies - Covers computational capabilities and varieties for renewable system design
Author: Saud Al-Fattah
Author: Kingshuk Srivastava
Author: Mohsen Hamoudia
Author: Yoshua Bengio
Author: Abdolhossein Hemmati-Sarapardeh
Author: Saud Al-Fattah
Author: Michael Negnevitsky
Author: Kingshuk Srivastava
Author: Vivienne Sze
Author: Ashutosh Kumar Dubey