Endüstri Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/204

Browse

Browsing Endüstri Mühendisliği Bölümü Koleksiyonu by Author "0000-0001-9786-6270"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Electricity Load Forecasting Using Deep Learning and Novel Hybrid Models
    (Sakarya University, 2022) Sütçü, Muhammed; Şahin, Kübra Nur; Koloğlu, Yunus; Çelikel, Mevlüt Emirhan; Gülbahar, İbrahim Tümay; 0000-0002-8523-9103; 0000-0001-9786-6270; 0000-0001-6198-569X; 0000-0001-9264-4345; 0000-0001-9192-0782; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Sütçü, Muhammed; Şahin, Kübra Nur; Koloğlu, Yunus; Çelikel, Mevlüt Emirhan; Gülbahar, İbrahim Tümay
    Load forecasting is an essential task which is executed by electricity retail companies. By predicting the demand accurately, companies can prevent waste of resources and blackouts. Load forecasting directly affect the financial of the company and the stability of the Turkish Electricity Market. This study is conducted with an electricity retail company, and main focus of the study is to build accurate models for load. Datasets with novel features are preprocessed, then deep learning models are built in order to achieve high accuracy for these problems. Furthermore, a novel method for solving regression problems with classification approach (discretization) is developed for this study. In order to obtain more robust model, an ensemble model is developed and the success of individual models are evaluated in comparison to each other
  • Thumbnail Image
    Article
    Probabilistic assessment of wind power plant energy potential through a copula-deep learning approach in decision trees
    (CELL PRESS, 2024) Şahin, Kübra Nur; Sutcu, Muhammed; 0000-0001-9786-6270; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Şahin, Kübra Nur
    In the face of environmental degradation and diminished energy resources, there is an urgent need for clean, affordable, and sustainable energy solutions, which highlights the importance of wind energy. In the global transition to renewable energy sources, wind power has emerged as a key player that is in line with the Paris Agreement, the Net Zero Target by 2050, and the UN 2030 Goals, especially SDG-7. It is critical to consider the variable and intermittent nature of wind to efficiently harness wind energy and evaluate its potential. Nonetheless, since wind energy is inherently variable and intermittent, a comprehensive assessment of a prospective site's wind power generation potential is required. This analysis is crucial for stakeholders and policymakers to make well-informed decisions because it helps them assess financial risks and choose the best locations for wind power plant installations. In this study, we introduce a framework based on Copula-Deep Learning within the context of decision trees. The main objective is to enhance the assessment of the wind power potential of a site by exploiting the intricate and non-linear dependencies among meteorological variables through the fusion of copulas and deep learning techniques. An empirical study was carried out using wind power plant data from Turkey. This dataset includes hourly power output measurements as well as comprehensive meteorological data for 2021. The results show that acknowledging and addressing the non-independence of variables through innovative frameworks like the Copula-LSTM based decision tree approach can significantly improve the accuracy and reliability of wind power plant potential assessment and analysis in other real-world data scenarios. The implications of this research extend beyond wind energy to inform decision-making processes critical for a sustainable energy future.