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• W3211113006 abstract "Nowadays, the increasing energy demand, development of smart grids, and the combination of different energy systems have led to the complexity of power systems. On the other hand, ever-expanding energy consumption, development of industry and technology systems, and high penetration of solar and wind energies have made electricity networks operate in more complex and uncertain conditions. Therefore, analysis of traditional power and energy systems requires physical modeling and extensive numerical computation. To analyze these systems’ behavior, advanced metering and condition monitoring devices and systems are utilized, which generate huge amounts of data. Assessment of these data is approximately impossible by conventional methods and requires powerful data mining procedures. Machine learning, deep learning, and a variety of regression, classification, and clustering algorithms are powerful tools to use in these issues. These procedures can be utilized for load/demand forecasting, demand response evaluation, defect/fault detection in electrical equipment, power system analysis and control, cybersecurity, and renewable energy generation prediction. Understanding the structure and functioning of each learning method is therefore one of the most important issues in the application of them to solve power system problems. In this chapter, we will introduce and discuss selected methods of data mining based on their learning, structure, formulation, mode of operation, and application in power systems. Literature on machine learning and deep learning procedures, train and test process of networked methods, and, finally, applications of each procedure are presented in this chapter." @default.
• W3211113006 created "2021-11-08" @default.
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• W3211113006 date "2021-01-01" @default.
• W3211113006 modified "2023-09-30" @default.
• W3211113006 title "Introduction to Machine Learning Methods in Energy Engineering" @default.
• W3211113006 cites W1495476169 @default.
• W3211113006 cites W1901113569 @default.
• W3211113006 cites W1965562387 @default.
• W3211113006 cites W1967379861 @default.
• W3211113006 cites W1991510770 @default.
• W3211113006 cites W1993882792 @default.
• W3211113006 cites W2027109892 @default.
• W3211113006 cites W2029836932 @default.
• W3211113006 cites W2046819118 @default.
• W3211113006 cites W2064675550 @default.
• W3211113006 cites W2073093536 @default.
• W3211113006 cites W2085740129 @default.
• W3211113006 cites W2089217930 @default.
• W3211113006 cites W2090287545 @default.
• W3211113006 cites W2107878631 @default.
• W3211113006 cites W2119821739 @default.
• W3211113006 cites W2126194848 @default.
• W3211113006 cites W2146200922 @default.
• W3211113006 cites W2149706766 @default.
• W3211113006 cites W2150913357 @default.
• W3211113006 cites W2156909104 @default.
• W3211113006 cites W222413954 @default.
• W3211113006 cites W2330219538 @default.
• W3211113006 cites W2339065330 @default.
• W3211113006 cites W2515896203 @default.
• W3211113006 cites W2521479021 @default.
• W3211113006 cites W2569349941 @default.
• W3211113006 cites W2604587500 @default.
• W3211113006 cites W2625224297 @default.
• W3211113006 cites W2625709058 @default.
• W3211113006 cites W2729222988 @default.
• W3211113006 cites W2745993090 @default.
• W3211113006 cites W2749305807 @default.
• W3211113006 cites W2754029504 @default.
• W3211113006 cites W2780628352 @default.
• W3211113006 cites W2783952819 @default.
• W3211113006 cites W2797025128 @default.
• W3211113006 cites W2884486887 @default.
• W3211113006 cites W2892841407 @default.
• W3211113006 cites W2893815961 @default.
• W3211113006 cites W2895868491 @default.
• W3211113006 cites W2900491283 @default.
• W3211113006 cites W2901862460 @default.
• W3211113006 cites W2914057541 @default.
• W3211113006 cites W2919115771 @default.
• W3211113006 cites W2920875710 @default.
• W3211113006 cites W2932013795 @default.
• W3211113006 cites W2933648389 @default.
• W3211113006 cites W2941184193 @default.
• W3211113006 cites W2942231644 @default.
• W3211113006 cites W2945566856 @default.
• W3211113006 cites W2945815269 @default.
• W3211113006 cites W2946396870 @default.
• W3211113006 cites W2949252504 @default.
• W3211113006 cites W2954012592 @default.
• W3211113006 cites W2964052759 @default.
• W3211113006 cites W2972958589 @default.
• W3211113006 cites W2977155375 @default.
• W3211113006 cites W2977783280 @default.
• W3211113006 cites W2984599380 @default.
• W3211113006 cites W2987147016 @default.
• W3211113006 cites W2988364570 @default.
• W3211113006 cites W3004454690 @default.
• W3211113006 cites W3007107472 @default.
• W3211113006 cites W3007605874 @default.
• W3211113006 cites W3016741933 @default.
• W3211113006 cites W3022218140 @default.
• W3211113006 cites W3023541969 @default.
• W3211113006 cites W3024280684 @default.
• W3211113006 cites W3030793642 @default.
• W3211113006 cites W3033591971 @default.
• W3211113006 cites W3033785644 @default.
• W3211113006 cites W3035254752 @default.
• W3211113006 cites W3043435009 @default.
• W3211113006 cites W3047313329 @default.
• W3211113006 cites W3053488909 @default.
• W3211113006 cites W3066024730 @default.
• W3211113006 cites W3081576544 @default.
• W3211113006 cites W3082558305 @default.
• W3211113006 cites W3107583705 @default.
• W3211113006 cites W3110303647 @default.
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• W3211113006 doi "https://doi.org/10.1007/978-3-030-77696-1_4" @default.
• W3211113006 hasPublicationYear "2021" @default.
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