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Article Citation - WoS: 16Citation - Scopus: 20Forecasting Air Quality in Tripoli: an Evaluation of Deep Learning Models for Hourly Pm2.5 Surface Mass Concentrations(Mdpi, 2023) Esager, Marwa Winis Misbah; Unlu, Kamil DemirberkIn this article, we aimed to study the forecasting of hourly PM2.5 surface mass concentrations in the city of Tripoli, Libya. We employed three state-of-the-art deep learning models, namely long short-term memory, gated recurrent unit, and convolutional neural networks, to forecast PM2.5 levels using univariate time series methodology. Our results revealed that the convolutional neural networks model performed the best, with a coefficient of variation of 99% and a mean absolute percentage error of 0.04. These findings provide valuable insights into the use of deep learning models for forecasting PM2.5 and can inform decision-making regarding air quality management in the city of Tripoli.Article Citation - WoS: 18Citation - Scopus: 21A Data-Driven Model To Forecast Multi-Step Ahead Time Series of Turkish Daily Electricity Load(Mdpi, 2022) Unlu, Kamil Demirberk; Ünlü, Kamil Demirberk; Ünlü, Kamil Demirberk; Industrial Engineering; Industrial EngineeringIt is critical to maintain a balance between the supply and the demand for electricity because of its non-storable feature. For power-producing facilities and traders, an electrical load is a piece of fundamental and vital information to have, particularly in terms of production planning, daily operations, and unit obligations, among other things. This study offers a deep learning methodology to model and forecast multistep daily Turkish electricity loads using the data between 5 January 2015, and 26 December 2021. One major reason for the growing popularity of deep learning is the creation of new and creative deep neural network topologies and significant computational advancements. Long Short-Term Memory (LSTM), Gated Recurrent Network, and Convolutional Neural Network are trained and compared to forecast 1 day to 7 days ahead of daily electricity load. Three different performance metrics including coefficient of determination (R-2), root mean squared error, and mean absolute error were used to evaluate the performance of the proposed algorithms. The forecasting results on the test set showed that the best performance is achieved by LSTM. The algorithm has an R-2 of 0.94 for 1 day ahead forecast, and the metric decreases to 0.73 in 7 days ahead forecast.Article Citation - WoS: 9Citation - Scopus: 9A Hybrid Deep Learning Methodology for Wind Power Forecasting Based on Attention(Taylor & Francis inc, 2024) Akbal, Yildirim; Unlu, Kamil DemirberkWind energy, as a sustainable energy source, poses challenges in terms of storage. Therefore, careful planning is crucial to utilize it efficiently. Deep learning algorithms are gaining popularity for analyzing complex time series data. However, as the "no free lunch" theorem suggests, the trade-off is: they need a lot of data to achieve the benefits. This even brings up a severe challenge for time series analysis, as the availability of historical data is often limited. This study aims to address this issue by proposing a novel shallow deep learning approach for wind power forecasting. The proposed model utilizes a fusion of transformers, convolutional and recurrent neural networks to efficiently handle several time series simultaneously. The empirical evidence demonstrates that the suggested innovative method exhibits exceptional forecasting performance, as indicated by a coefficient of determination (R2) of 0.99. When the forecasting horizon reaches 48, the model's performance declines significantly. However, when dealing with long ranges, utilizing the mean as a metric rather than individual point estimates would yield superior results. Even when forecasting up to 96 hrs in advance, obtaining an R2 value of 0.50 is considered a noteworthy accomplishment in the context of average forecasting.
