Hybrid Forecasting for Sustainable Electricity Demand in The Netherlands Using SARIMAX, SARIMAX-LSTM, and Sequence-to-Sequence Deep Learning Models

Duaa Ashtar, Seyed Sahand Mohammadi Ziabari () and Ali Mohammed Mansoor Alsahag
Additional contact information
Duaa Ashtar: Informatics Institute, University of Amsterdam, Science Park, 1098 XH Amsterdam, The Netherlands
Seyed Sahand Mohammadi Ziabari: Informatics Institute, University of Amsterdam, Science Park, 1098 XH Amsterdam, The Netherlands
Ali Mohammed Mansoor Alsahag: Informatics Institute, University of Amsterdam, Science Park, 1098 XH Amsterdam, The Netherlands

Sustainability, 2025, vol. 17, issue 16, 1-22

Abstract: Accurate forecasting is essential for effective energy management, particularly in evolving and data-driven electricity markets. To address the increasing complexity of national energy planning in The Netherlands, this study proposes a hybrid multi-stage forecasting framework to improve both short- and long-term electricity demand predictions. We compare three model types, classical statistical (SARIMAX), hybrid statistical–deep learning (SARIMAX–LSTM), and deep learning (sequence-to-sequence), across forecasting horizons from 1 to 180 days. The models are trained on daily load data from ENTSO-E (2009–2023), incorporating exogenous variables such as weather conditions, energy prices, and socioeconomic indicators, as well as engineered temporal features such as calendar effects, seasonal patterns, and rolling demand statistics. Three feature configurations were tested: exogenous-only, generated-only, and a combined set. Internally generated features consistently outperformed exogenous inputs, especially for long-term forecasts. The sequence-to-sequence model achieved the highest accuracy at the 180-day horizon, with a mean absolute percentage error (MAPE) of approximately 1.88%, outperforming both SARIMAX and the SARIMAX–LSTM hybrid models. An additional SARIMAX-based analysis assessed the individual effects of renewable and socioeconomic indicators. Renewable energy production improved short-term accuracy (MAPE reduced from 2.13% to 1.09%) but contributed little to long-term forecasting. Socioeconomic variables had limited predictive value and, in some cases, slightly reduced accuracy, particularly over long-term horizons.

Keywords: energy demand; time series; renewable energy; machine learning; long short-term memory; SARIMAX; sequence-to-sequence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/16/7192/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/16/7192/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:16:p:7192-:d:1720585

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().