Abstract
The current paper is focusing on daily load consumption forecasting. This is a very important issue for every distribution system operator. Several methods are applied to achieve this goal: artificial neural networks and three conventional methods. The latter ones are based on linear approximation, curve fitting and decision. The starting point is represented by a big data set belonging to a real distribution system operator within our country. Thus, the authors are dealing with real data, extracted from the distribution network. In the following, a software tool has been developed, in Matlab environment for artificial intelligence based method and also for the conventional ones. A huge amount of data has been processed and the forecast has been performed for all the distribution branches belonging to that operator. Several indices have been computed in order to be able to provide related comments. Once all the forecasts have been carried-out, detailed analyses have been performed. Also, a hierarchy based on performance characteristics has been provided.
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Acknowledgment
This work was partial supported by the Politehnica University Timisoara research grants PCD-TC-2017.
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Barbulescu, C., Kilyeni, S., Chis, V., Craciun, M., Simo, A. (2021). Daily Load Curve Forecasting. Comparative Analysis: Conventional vs. Unconventional Methods. In: Balas, V., Jain, L., Balas, M., Shahbazova, S. (eds) Soft Computing Applications. SOFA 2018. Advances in Intelligent Systems and Computing, vol 1221. Springer, Cham. https://doi.org/10.1007/978-3-030-51992-6_1
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DOI: https://doi.org/10.1007/978-3-030-51992-6_1
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