- Tytuł:
-
Comparative analysis of methods for hourly electricity demand forecasting in the absence of data - a case study
Analiza porównawcza metod prognozowania godzinnego zapotrzebowania na energię elektryczną przy brakach w danych - studium przypadku - Autorzy:
- Zawadzki, Jan
- Powiązania:
- https://bibliotekanauki.pl/articles/2194900.pdf
- Data publikacji:
- 2023
- Wydawca:
- Akademia Bialska Nauk Stosowanych im. Jana Pawła II w Białej Podlaskiej
- Tematy:
-
forecasting
missing data
time series
high frequency - Opis:
- Scope and purpose of work: This paper examines the impact of the number of gaps in data, the analytical form, and the model type selection criterion on the accuracy of interpolation and extrapolation forecasts for hourly data. Materials and methods: Forecasts were developed on the basis of predictors that are based on: classical time series forecasting models and regression time series forecasting models, hybrid time series forecasting models and hybrid regression forecasting models for uncleared series, and exponential smoothing models for cleared series of two or three types of seasonal fluctuations, with minimum estimates of errors in interpolation or extrapolation forecasts. Results: Adaptive and hybrid regression models have proved to have the most favorable predictive properties. Most hybrid time series models for systematic and non-systematic gaps and for both analytical forms are single models that generally describe fluctuations within a 24-hour cycle. Conclusions: The lowest estimators of prediction errors involving interpolation were obtained for exponential smoothing models, followed by hybrid regression models. A reverse sequence was obtained for extrapolative forecasting.
- Źródło:
-
Economic and Regional Studies; 2023, 16, 1; 34-50
2083-3725
2451-182X - Pojawia się w:
- Economic and Regional Studies
- Dostawca treści:
- Biblioteka Nauki
Artykuł