Browsing by Author "Copur, Onur"

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Comparative Study of Forecasting Models for COVID-19 Outbreak in Turkey
(Institute of Electrical and Electronics Engineers Inc., 2021) Mert Nakıp; Onur Çopur; Cüneyt Güzeliş; Guzelis, Cuneyt; Nakip, Mert; Copur, Onur
This paper gives an explanation for the failure of machine learning models for the prediction of the cases and the other future trends of Covid-19 pandemic. The paper shows that simple Linear Regression models provide high prediction accuracy values reliably but only for a 2-weeks period and that relatively complex machine learning models which have the potential of learning long-term predictions with low errors cannot achieve to obtain good predictions with possessing a high generalization ability. It is suggested in the paper that the lack of a sufficient number of samples is the source of the low prediction performance of the forecasting models. To exploit the information which is of most relevant with the active cases we perform feature selection over a variety of variables such as the numbers of active cases deaths recoveries and population. Furthermore we compare Linear Regression Multi-Layer Perceptron and Long-Short Term Memory models each of which is used for prediction of active cases together with various feature selection methods. Our results show that the accurate forecasting of the active cases with high generalization ability is possible up to 3 days because of the small sample size of COVID-19 data. We observe that the Linear Regression model has much better prediction performance with high generalization ability as compared to the complex models but as expected its performance decays sharply for more than 14-days prediction horizons. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 30
Citation - Scopus: 40
Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network
(Elsevier Ltd, 2023) Mert Nakıp; Onur Çopur; Emrah Biyik; Cüneyt Güzeliş; Copur, Onur; Guzelis, Cuneyt; Nakip, Mert; Biyik, Emrah
Smart home energy management systems help the distribution grid operate more efficiently and reliably and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting optimization and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES) to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling 37.5 times faster than the optimization while outperforming state-of-the-art forecasting techniques. © 2023 Elsevier B.V. All rights reserved.