Electromagnetic pollution measurement in the system rooms of a university

Etem, Taha; Pala, Zeydin; Bozkurt, Ilyas

doi:10.1109/memstech.2017.7937548

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…In this study, time series, which have a very wide application area [4], [5], [14]- [22], [6]- [13] , were used. Low-carbon consumption data of United States, Russia, China, and Japan used in the forecasting of time series, were retrieved from the website https://ourworldindata.org/energy.…”

Section: Methodsmentioning

confidence: 99%

A Historical Overview of Low-Carbon Energy Consumption in the USA, Russia, China, and Japan

2023

Proceeding Book of 3rd International Conference on Scientific and Academic Research ICSAR 2023

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This article provides a comprehensive summary of the historical trajectories of low-carbon energy consumption in four major global economies the United States, Russia, China, and Japan from 1965 to 2022. Tracking the evolution of these nations' approaches to low-carbon energy is crucial for understanding their contributions to global sustainability goals and the diverse strategies employed in response to changing energy landscapes. In this study, future predictions were made regarding the lowcarbon energy consumption of United States, Russia, China, and Japan between 1965-2022 with the help of R-based Auto.Arima, TBATS, ETS and THETAF statistical models. Prediction results were evaluated with the Mean Absolute Percentage Error (MAPE) metric.

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Section: Methodsmentioning

confidence: 99%

A Historical Overview of Low-Carbon Energy Consumption in the USA, Russia, China, and Japan

2023

Proceeding Book of 3rd International Conference on Scientific and Academic Research ICSAR 2023

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“…This type of data has a variety of applications in many different fields and industries. Some areas where time series are used: These can be listed as weather (Hossain et al, 2015), health , speech recognition (Srivastava et al, 2014), energy consumption (Hwang & Yoo, 2014;Mohsin et al, 2022), radiation predictions (Etem et al, 2017;, sunspot prediction (C. Chen et al, 2010;Zeydin Pala & Atici, 2019), natural gas production prediction (N. Li et al, 2021;Zeydin Pala, 2023), sensor data analysis (Dhillon et al, 2020).…”

Section: Time Series Analysismentioning

confidence: 99%

“…The ability to capture temporal dependencies allows for precise short-term and long-term forecasting, aiding in optimal resource allocation, grid planning, and energy market operations. The uses of time series models extend beyond electricity generation to various fields, including finance, economics, climate science, weather (Hossain, Rekabdar, Louis, & Dascalu, 2015), health , speech recognition (Srivastava, Hinton, Krizhevsky, Sutskever, & Salakhutdinov, 2014), energy consumption (Hwang & Yoo, 2014;Mohsin, Naseem, Sarfraz, & Azam, 2022), radiation predictions (Etem, Pala, & Bozkurt, 2017;Z. Pala, Ünlük, & Yaldız, 2019), sunspot prediction (C. Chen et al, 2010;Zeydin Pala & Atici, 2019), natural gas production prediction (N. Li, Wang, Wu, Zeydin Pala, 2023), and sensor data analysis (Dhillon, Madhu, Kaur, & Singh, 2020) showcasing their versatility in capturing and predicting sequential data patterns.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Time Series Analysis of Annual Fossil-Based Electrical Energy Production Across Key Nations (1985-2022)

Pala

2024

Mitteilungen Klosterneuburg

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This study addresses the critical challenge of accurately predicting annual electrical energy production derived from fossil sources across diverse nations, including Türkiye, Germany, England, France, Iran, and Ukraine spanning the years 1985 to 2022. Employing a combination of advanced deep learning techniques and traditional statistical models, the research aims to enhance forecasting precision and contribute to a deeper understanding of global energy consumption dynamics. By leveraging time series analysis, the study captures the intricate temporal patterns inherent in fossilbased energy production data, enabling robust predictions over an extended historical timeframe. Performance evaluation metrics such as Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are utilized to rigorously assess the accuracy and reliability of the developed forecasting models. The scientific significance of this study lies in its ability to provide valuable insights into the complex interplay between socio-economic factors, technological advancements, and environmental considerations shaping fossil-based energy production trends. By conducting a comparative analysis across multiple countries with distinct energy landscapes, the research not only elucidates regional variations but also underscores the need for tailored forecasting approaches to address unique contextual challenges. The findings of this study hold significant implications for energy policymakers, industry stakeholders, and researchers striving to optimize resource allocation, mitigate environmental impacts, and transition towards sustainable energy systems. By advancing the state-of-the-art in energy forecasting methodologies, this research contributes to the broader goal of achieving a more resilient, equitable, and environmentally sustainable energy future.

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“…This type of data has a variety of applications in many different fields and industries. Some areas where time series are used: These can be listed as weather (Hossain, Rekabdar, Louis, & Dascalu, 2015), health , speech recognition (Srivastava, Hinton, Krizhevsky, Sutskever, & Salakhutdinov, 2014), energy consumption (Hwang & Yoo, 2014;Mohsin, Naseem, Sarfraz, & Azam, 2022), radiation predictions (Etem, Pala, & Bozkurt, 2017;Z. Pala, Ünlük, & Yaldız, 2019), sunspot prediction (C. Chen et al, 2010;Zeydin Pala & Atici, 2019), natural gas production prediction (N. Li, Wang, Wu, & Bentley, 2021;Zeydin Pala, 2023), sensor data analysis (Dhillon, Madhu, Kaur, & Singh, 2020).,energy production (Zeydin Pala, 2024).…”

Section: Time Series Analysismentioning

confidence: 99%

Long-Term Prediction of Climate Variables of the Lake Van Basin: A Comprehensive Analysis Using Statistical and Deep Learning Models

Pala,

Şevgin

2024

Mitteilungen Klosterneuburg

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The aim of the present study is to predict environmental conditions in the basin of Lake Van in the Eastern Anatolia, Türkiye. The datasets for the period between January 1950 and June 2023 that include critical parameters like sunshine hours, temperature, precipitation, and humidity were used for predicting. The research developed a two-way approach that combines statistical-based models which include Auto.Arima, SARIMA and TBATS models together with deep learning-based models like NNTAR, MLP and ELM. The model's accuracy and reliability are being evaluated by using the test lengths of 60, 72, and 84 months periods during the forecasting process. Evaluation metrics, especially the root mean square error (RMSE) and mean absolute error (MAE) were used to numerically show the capability of every model. This trial is a major contribution to environmental forecasting literature by focusing on a particular geographical zone, ensuring environment parameters diversity, employing diverse prediction models, and offering a long-term view. The conclusions have been proved meaningful for researchers and environmentalists who dedicate their time to climate science, environmental management, and sustainable development in the Eastern Anatolia region of Türkiye

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Electromagnetic pollution measurement in the system rooms of a university

Cited by 4 publications

References 2 publications

A Historical Overview of Low-Carbon Energy Consumption in the USA, Russia, China, and Japan

A Historical Overview of Low-Carbon Energy Consumption in the USA, Russia, China, and Japan

A Comprehensive Time Series Analysis of Annual Fossil-Based Electrical Energy Production Across Key Nations (1985-2022)

Long-Term Prediction of Climate Variables of the Lake Van Basin: A Comprehensive Analysis Using Statistical and Deep Learning Models

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