Abstract
It is widely known that the generation and consumption of electricity should be balanced for secure operation and maintenance of the electricity grid. In order to help achieve this balance in the grid, the renewable energy resources such as wind and stream-flow should be forecast at high accuracies on the generation side, and similarly, electricity consumption should be forecast using a high-performance system. In this paper, we deal with short-term electricity consumption forecast in Turkey, and conduct various ANN-based experiments using real consumption data. The experiments are carried out on datasets of various scales in order to arrive at a learning system that uses, as the training dataset, a convenient subset of large quantities of field data. Thereby, the performance of system can be improved in addition to decreasing the time for the training stage, so that the resulting system can be efficiently used in operational settings. The performance evaluation results of these experiments to forecast electricity consumption in Nigde province of Turkey are presented together with the related discussions. This study provides an important baseline of findings, upon which other learning systems and training settings can be tested, improved, and compared with each other.
This study is carried out within the scope of the Dispatcher Information System Project (5172801) developed for TEİAŞ by TÜBİTAK MRC Energy Institute.
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Arslan, Y. et al. (2018). Short-Term Electricity Consumption Forecast Using Datasets of Various Granularities. In: Woon, W., Aung, Z., Catalina Feliú, A., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. Technologies, Systems and Society. DARE 2018. Lecture Notes in Computer Science(), vol 11325. Springer, Cham. https://doi.org/10.1007/978-3-030-04303-2_9
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DOI: https://doi.org/10.1007/978-3-030-04303-2_9
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