E3S Web Conf.
Volume 210, 2020Innovative Technologies in Science and Education (ITSE-2020)
|Number of page(s)||9|
|Published online||04 December 2020|
Economic factors of electricity transport based on energy consumption forecasting
1 Vyatka State University, Moskovskaya, 36, Kirov, 610000, Russia
2 Vyatka State Agricultural Academy, October Avenue, 133, Kirov, 610017, Russia
3 Moscow State University of Civil Engineering, 26, Yaroslavskoye Shosse, 129337, Moscow, Russia
* Corresponding author: firstname.lastname@example.org
Forecasting significance in the energy market is extremely high. Demand for electricity determines the key decisions on its purchase and production, load transfer and transmission control. Over the past few decades, several methods have been developed to accurately predict the future of energy consumption. This article discusses various methods for forecasting energy demand. Three blocks of methods are considered: statistical, methods using artificial intelligence and hybrid. Authors defined the metrics that show the quality of the models and help to compare the results of the models: mean absolute error (MAE), mean absolute percentage error (MAPE), root-mean-square deviation (RMSE), minimum and maximum errors on the test sample. A comparative analysis of forecasting methods has been lunched on the open data set. The best result is obtained using a combined model based on the Lasso regression method. The accuracy and speed of predictions helps to get an economic effect from regulating generation by selling electricity at the peak of consumption.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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