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    Electricity Demand Prediction: Case of Turkey
    (Springer, 2023) Polat, Ezgi; Aydın, Nezir; Işıklı, Erkan; Kayakutlu, Gülgün; Kayalıca, M. Özgür
    It is critical to generate accurate power demand forecasts for balancing supply and demand, as well as for the efficient operation and management of electricity generation. Accurate forecasts can also help to prevent energy wastage, reduce operating costs, and minimize environmental impacts. Several approaches for this task have been proposed in the related literature, including machine learning, statistical modeling, stochastic/fuzzy/gray modeling, and metaheuristics. The present study initially discusses the importance of accurately predicting electricity demand, providing an extensive overview of linear functions, neural network architectures, and regression models concerning their relevant concepts and applications, and then furnishes a case study of monthly electricity demand prediction in Turkey using data from 1998 to 2017. Considering several independent variables (IVs), namely average temperature, average precipitation, consumer price index, total industrial production index, total import, and total export, the performance of an Artificial Neural Network (ANN) model was compared to that of a Multiple Linear Regression (MLR) model and an Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model. The former has been shown to outperform the other two in terms of MAPE and R2, indicating its capability to better explain the nonlinear relationship between the IVs and energy consumption. As a potential avenue for future research, to develop more comprehensive models that can generate reliable forecasts, the impacts of weather and climate change on energy demand should be evaluated in different countries and regions adopting hybrid models that combine different methodologies such as metaheuristics and machine learning.
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    Performance of airlines: topsis analysis
    (PressAcademia, 2022-07-30) Teker, Dilek; Teker, Suat; Polat, Ezgi
    Purpose- Airline industry is very important for modern society and has a crucial role for globalization and business development by connecting regions, promoting global trade and tourism and hence enabling economic and social development. Over the last two decades, the troubled airlines were oftenly on news delebrating financial difficulties, layouts and distrupted sceduled flights. Covid-19 also has a crucial impact on the deterioration of the industry due to quarantinas and break downs for travel and business. This empricial research intends to analyse and rank the financial performance of the top 11 airlines in the Word for the periods of 2019-2020-2021 (Covid era) and also group and rank these airlines as US Airlines, European Airlines and Chinese Airlines. Methodology- This study employs TOPSIS method. 11 biggest airline companies are selected by the total revenue generated in 2021. A total of 18 financial indicators are used to measure the profitability, liquidity, financial structure and operational efficiency for the period of 2019- 2021. Utilizing a TOPSIS analysis, the performance scores of these airline companies are calculated and a ranking is determined according to these scores. Findings- United Airlines, Delta Airlines and Southwest Airlines are the top performers in 2019. These airlines have a big drop in 2020 and their performance ranks are 8, 10 and 7, respectively. The best performer in 2020 is the Turkish Airlines. Also, United Airlines and Southwest Airlines show a fast recovery in 2021 by ranking 2 and 1, respectively. When performance ranking is applied for the groups of companies (namely; USA based, Europe based and China based companies), the USA based group is the best performer in 2019, the worst performer in 2020 and the second best performer in 2021. Meanwhile, China based group are the best performer in 2020 and 2021. Conclusion- This study shows that the Covid era has affected profitability and operational efficiency of airline companies significantly. As a result of the study, it is observed that China based airline companies managed the Covid era better than the USA based and European based airline companies.