Innovative machine learning approaches for indoor air temperature forecasting in smart infrastructure

Innovative machine learning approaches for indoor air temperature forecasting in smart infrastructure

Abstract Efficient energy management and maintaining an optimal indoor climate in buildings are critical tasks in today’s world. This paper presents an innovative approach to surrogate modeling for predicting indoor air temperature (IAT) in buildings, leveraging advanced machine learning techniques....

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Bibliographic Details
Main Authors: Nataliya Shakhovska, Lesia Mochurad, Rosana Caro, Sotirios Argyroudis
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Surrogate modeling
Time series forecasting
LSTM
Energy efficiency
Smart buildings
Cumulative error analysis
Online Access:https://doi.org/10.1038/s41598-024-85026-3
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https://doi.org/10.1038/s41598-024-85026-3

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