Yazar "Bulus, Kurtulus" seçeneğine göre listele

Listeleniyor 1 - 4 / 4
  • [ X ]
    Öğe
    A benchmark of GRU and LSTM networks for short-term electric load forecasting
    (Institute of Electrical and Electronics Engineers Inc., 2021) Zor, Kasim; Bulus, Kurtulus
    Recently, electric power systems have been modernised to be integrated with distributed energy systems having intermittent characteristics. Herein, short-term electric load forecasting (STLF), which covers hour, day, or week-ahead predictions of electric loads, is a crucial piece of the modern power system puzzle whose level of complexity has become more and more sophisticated owing to incorporating microgrids and smart grids. Due to the nonlinear feature of electric loads and the uncertainties in the modern power systems, deep learning algorithms are frequently applied to STLF problem which can be described as an arduous challenge because of being affected by several impacts. In this paper, gated recurrent unit (GRU) and long short-term memory (LSTM) networks are implemented in forecasting an hour-ahead electric loads of a large hospital complex located in Adana, Turkey. Overall results belonging to the benchmark of GRU and LSTM networks for STLF revealed that employing GRU networks performed better in terms of mean absolute percentage error (MAPE) by 7.8% and computational time by 15.5% in comparison with utilising LSTM networks. © 2021 IEEE.
  • [ X ]
    Öğe
    A hybrid deep learning algorithm for short-term electric load forecasting
    (IEEE, 2021) Bulus, Kurtulus; Zor, Kasim
    Over the last two decades, electric load forecasting has strengthened its significant role in electric power systems due to equalising the vital balance between generation and consumption of electrical energy for all actors of deregulated electricity markets. Artificial intelligence-based techniques are frequently used for short-term electric load forecasting owing to the abstruse nature of electric loads that can be influenced by a variety of factors. In this paper, a novel hybrid deep learning algorithm that combines GMDH and GRU networks is meticulously applied for one hour-ahead load forecasting of a large hospital complex. In the proposed algorithm, GMDH and GRU networks are employed for feature selection and prediction respectively. Consequently, the obtained results have demonstrated that the proposed algorithm is capable of reducing mean absolute percentage error by 12% and computational time by 5%.
  • [ X ]
    Öğe
    A novelty detection approach to classification of breast tissue containing microcalcifications
    (Association for Computing Machinery, Inc, 2017) Avsar, Ercan; Bulus, Kurtulus
    Appearance of microcalcifications in mammograms is one of the early signs of breast cancer. In this work, one-class support vector machines (SVM), a novelty detection method, is utilized for detection of the mammogram samples containing microcalcifications. These samples are small regions of the mammograms with the size of 25x25 pixels. Each of the samples are represented by 25 features that are already proven to be accurate identifiers of the microcalcifications. Since the obtained classification performance of one-class SVM with all these 25 features is very low (accuracy = 0.5575, sensitivity = 0.2107, specificity = 0.9042), number of these features is reduced by using principal component analysis (PCA). Training a classifier only with the PCA features achieves an improved performance (accuracy = 0.9464, sensitivity = 1.0000, specificity = 0.8927) where the number of false negative samples is reduced from 206 to 0. © 2017 Association for Computing Machinery.
  • [ X ]
    Öğe
    Medium- to long-term nickel price forecasting using LSTM and GRU networks
    (Elsevier Sci Ltd, 2022) Ozdemir, Ali Can; Bulus, Kurtulus; Zor, Kasim
    Recently, nickel is a critical metal for manufacturing stainless steel, rechargeable electric vehicle batteries, and alloys utilized in the state-of-the-art technologies. The use of more environmentally friendly electric vehicles has become widespread and brought tackling climate change to forefront, especially for reducing greenhouse gas emissions. Therefore, the demand for rechargeable batteries that power electric vehicles and the need for the nickel in the production of these batteries will increase as well. In addition to those, nickel prices significantly impact mine investment decisions, mine planning, economic development of nickel companies, and countries that depend on nickel resources. However, there is uncertainty about how the nickel price will trend in the future, and the solution to this problem attracts the attention of researchers. For forecasting nickel price, this paper proposes recurrent neural networks-based on long short-term memory (LSTM) and gated recurrent unit (GRU) networks, classified as deep learning algorithms. Mean absolute percentage error (MAPE) was used as the performance measure to compute the accuracy of the proposed techniques. As a result, it has been determined that the LSTM and GRU networks are very useful and successful in forecasting the nickel price variations owing to having average MAPE values of 7.060% and 6.986%, respectively. Furthermore, it has been observed that GRU networks surpassed the LSTM networks by 33% in terms of average computational time.