Economic factors of electricity transport based on energy consumption forecasting
| Autor: | Olga Soboleva, Anna Grabar, Tatyana Kondratyeva, Darya Starkova |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Consumption (economics)
lcsh:GE1-350 Mean squared error business.industry Computer science 0211 other engineering and technologies 02 engineering and technology Energy consumption 010501 environmental sciences 01 natural sciences Mean absolute percentage error Econometrics Production (economics) Energy market 021108 energy Electricity business Energy (signal processing) lcsh:Environmental sciences 0105 earth and related environmental sciences |
| Zdroj: | E3S Web of Conferences, Vol 210, p 13036 (2020) |
| ISSN: | 2267-1242 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|