دانلود مقاله هوش مصنوعی و یادگیری ماشین در سیستم های انرژی
چکیده
1. مقدمه
2. روش ها و رویکردهای رایج یادگیری ماشین
3. روش شناسی
4. نتایج و بحث
5. نتیجه گیری
اعلامیه منافع رقابتی
در دسترس بودن داده ها
منابع
Abstract
1. Introduction
2. Common machine learning methods and approaches
3. Methodology
4. Results and discussion
5. Conclusion
Declaration of competing interest
Data availability
References
چکیده
همه گیری COVID-19 باعث انحرافات شدید از زندگی روزمره شده است. هدف از این مطالعه بررسی این موضوع بود که چگونه این انحرافات بر حس انسجام و سطح پرخاشگری نوجوانان تأثیر می‌گذارد و آیا این موضوع تحت تأثیر رابطه آنها با حیوانات، به‌ویژه اسب‌ها بوده است. در دو نمونه تصادفی از دانش‌آموزان مدارس فنی و حرفه‌ای در مجارستان، که در ژوئن 2018 و ژوئن 2020 انتخاب شدند (n1 = 525، n2 = 412)، گروه‌های جداگانه‌ای از افرادی که به طور منظم در فعالیت‌های کمک اسب (ES) شرکت کرده بودند، انتخاب شدند. قبل از همه گیری (OS) نداشت. داده ها با استفاده از یک پرسشنامه ناشناس و کاغذی و در طول همه گیری یک نسخه آنلاین از مقیاس حس انسجام (SOC13) و برایانت اسمیت (B12) جمع آوری شد. در طول همه گیری، حس انسجام پسران ضعیف شد و پرخاشگری آنها افزایش یافت. تجزیه و تحلیل رگرسیون خطی چندگانه نشان داد که، صرف نظر از جنسیت و گروه سنی، افزایش زمان صرف شده برای استفاده از اینترنت (0.001 > P)، کمبود همکلاسی (0.017 = p)، کاهش زمان صرف شده در خارج از منزل (p = 0.026) و کاهش فعالیت بدنی (P = 0.001). P <0.038) در طول همه گیری به طور قابل توجهی تمایل به رفتار پرخاشگرانه را افزایش داد، در حالی که بودن با اسب یا حیوان خانگی مفید بود (p <0.001). تغییرات اعمال شده توسط منع رفت و آمد توسط 90 درصد دانش آموزان بد ارزیابی شد، با این حال، آنهایی که حس انسجام قوی داشتند نسبت به آنها احساس منفی کمتری داشتند. مدارس باید بر تقویت حس انسجام در دانش آموزان تاکید زیادی داشته باشند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Economic development and the comfort-loving nature of human beings in recent years have resulted in increased energy demand. Since energy resources are scarce and should be preserved for future generations, optimizing energy systems is ideal. Still, due to the complexity of integrated energy systems, such a feat is by no means easy. Here is where computer-aided decision-making can be very game-changing in determining the optimum point for supply and demand. The concept of artificial intelligence (AI) and machine learning (ML) was born in the twentieth century to enable computers to simulate humans' learning and decision-making capabilities. Since then, data mining and artificial intelligence have become increasingly essential areas in many different research fields. Naturally, the energy section is one area where artificial intelligence and machine learning can be very beneficial. This paper uses the VOSviewer software to investigate and review the usage of artificial intelligence and machine learning in the energy field and proposes promising yet neglected or unexplored areas in which these concepts can be used. To achieve this, the 2000 most recent papers in addition to the 2000 most cited ones in different energy-related keywords were studied and their relationship to AI- and ML-related keywords was visualized. The results revealed different research trends in recent years from the basic to more cutting-edge topics and revealed many promising areas that are yet to be explored. Results also showed that from the commercial aspect, patents submitted for artificial intelligence and machine learning in energy-related areas had a sharp increase.
Introduction
Economic development and increasing welfare are always entangled with the rising consumption of energy resources. Increasing energy generation as the default answer to how to cope with this additive energy consumption may not be the best answer. From an energy justice perspective, it’s not acceptable to deplete energy reservoirs that belong to the next generations [1]. Although retrofitting existing equipment and minimizing energy usage by combining (or cascading) multiple systems for increased efficiency may be an answer to this challenge, increasing efficiency may not be the ideal solution. It’s valid that efficiency leads to lower consumption, but it should be noted that many of these systems might also be able to be turned off or operate on lower loads. So, a more dynamic approach may be a better solution.
Artificial intelligence and machine learning are relatively new concepts in energy that can be promising tools to operate systems by implementing past and predicted futures to increase the effectiveness of systems.
Conclusion
The concept of artificial intelligence (AI) and machine learning (ML) is for computers to simulate humans’ learning and decision-making capabilities. With advances in computing systems, AI and ML have become increasingly important areas in many different branches of science and industry. The energy section is also one of the areas that can benefit from AI and ML. To investigate the current standing point of AI and ML in energy-related areas, we used VOSviewer software to investigate and review the relatively new usage of AI and ML in the energy field and propose promising or neglected areas in which these concepts can be used.
The results showed that from 2000 AI gains an increasing focus, especially after 2014 when the number of articles in ML skyrocketed. And in 6 years become 10 times more compared to 2014. Although there have been many papers in different fields of energy that introduced new usages of AI and ML in that section, due to the vastness of energy usage and respected fields of it, obviously, in no way the available articles could cover all these areas.