Improving heat demand forecasting with feature reduction in an Encoder–Decoder LSTM model

Accurate short-term heat demand forecasting is essential for the efficient operation of District Heating Networks (DHNs). However, many forecasting models rely on large sets of engineered or externally forecasted variables, introducing redundancy, computational overhead, and reduced generalizability...

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Bibliographic Details

Published in:	Energy reports Vol. 14; pp. 5048 - 5060
Main Authors:	Darbandi, Amin, Brockmann, Gerrid, Kriegel, Martin
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.12.2025
Subjects:	Artificial Intelligence Data-driven prediction Demand forecast Machine learning Data-driven prediction Artificial Intelligence Demand forecast Machine learning
ISSN:	2352-4847, 2352-4847
Online Access:	Get full text
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