Short-Term Electricity Consumption Forecast using Datasets of Various Granularities

Date

2018-09-10

Author

ARSLAN, YUSUF
ŞİMŞEK DİLBAZ, AYBİKE
Ertekin Bolelli, Şeyda
Karagöz, Pınar
Birtürk, Ayşe Nur
Eren, Sinan
KÜÇÜK, DİLEK

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Electricity consumption forecast, Load forecast, Neural nets, Data mining

URI

https://hdl.handle.net/11511/39647

DOI

https://doi.org/10.1007/978-3-030-04303-2_9

Conference Name

International Workshop on Data Analytics for Renewable Energy Integration

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

Y. ARSLAN et al., “Short-Term Electricity Consumption Forecast using Datasets of Various Granularities,” presented at the International Workshop on Data Analytics for Renewable Energy Integration, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39647.