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Copyright of Journal of Selçuk University Social Sciences Vocational School is the property of Journal of Selcuk University Social Sciences Vocational School and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Aksaray University Faculty of Economics & Administrative Sciences / Aksaray Üniversitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi is the property of Aksaray University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Gazi Journal of Economics & Business is the property of Gazi Journal of Economics & Business and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Supply-demand management of the energy sector is of prime importance for rapidly growing economies and developing countries. Depending on the increasing population, the rising economy, and the developments in the industry, the countries' energy needs are increasing to a great extent. Therefore, using models to accurately and reliably predict future electricity supply-demand trends has attracted the attention of consumers and investors in this field. In this study, Artificial Neural Networks, Ridge Regression, Lasso Regression and Support Vector Regression, proven successes in the literature, are used to realize Turkey's short-term electric load demand estimation. Data used in the forecasting models were obtained from the Turkish Electricity Transmission Corporation. A one-hour future estimation is accomplished using a past year-long dataset of electrical energy. To compare the results obtained from the methods, RMSE, MAE and R2 values, frequently used in the literature, were calculated. The comparison results show that the Artificial Neural Networks were more successful with RMSE=0.86, MAE=0.62 and R2=0.97 results among the developed machine learning models. [ABSTRACT FROM AUTHOR]