<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Scope and purpose of work</h3>
<p>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.</p>
</sec>
<sec><h3>Materials and methods</h3>
<p>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.</p>
</sec>
<sec><h3>Results</h3>
<p>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.</p>
</sec>
<sec><h3>Conclusions</h3>
<p>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.</p>
</sec>
</abstract>

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.

Comparative Analysis of Methods for Hourly Electricity Demand Forecasting in the Absence of Data – A Case Study

Faculty of Economics, Department of Applications of Mathematics in Economics Professor emeritus

West Pomeranian University of Technology in Szczecin

Economic and Regional Studies / Studia Ekonomiczne i Regionalne

This work is licensed under the Creative Commons Attribution 4.0 International License.

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...

Comparative Analysis of Methods for Hourly Electricity Demand Forecasting in the Absence of Data – A Case Study

Online veröffentlicht: 31. März 2023

Seitenbereich: 34 - 50

Eingereicht: 01. Nov. 2022

Akzeptiert: 01. Jan. 2023

DOI: https://doi.org/10.2478/ers-2023-0003

Schlüsselwörter
forecasting, missing data, time series, high frequency

© 2023 Jan Zawadzki, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Comparative Analysis of Methods for Hourly Electricity Demand Forecasting in the Absence of Data – A Case Study

Online veröffentlicht: 31. März 2023

Seitenbereich: 34 - 50

Eingereicht: 01. Nov. 2022

Akzeptiert: 01. Jan. 2023

DOI: https://doi.org/10.2478/ers-2023-0003

Schlüsselwörterforecasting, missing data, time series, high frequency

© 2023 Jan Zawadzki, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
forecasting, missing data, time series, high frequency