Amismart an advanced metering infrastructure for power consumption monitoring and forecasting in smart buildings

Abstract Load forecasting is considered to be the core of an efficient predictive energy management for buildings. In this context, the deployment of smart meters and sensors enabled continuous energy usage monitoring in modern buildings. This streaming data led to the development of a new Online Ho...

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Vydané v:Discover Computing Ročník 28; číslo 1; s. 1 - 28
Hlavní autori: Sarah Hadri, Mehdi Najib, Mohamed Bakhouya, Youssef Fakhri, Mohamed El aroussi, Zaradatcht Taifour, Jaafar Gaber
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Springer 14.06.2025
Predmet:
Advanced smart metering infrastructure
Energy efficient buildings
Machine learning
Online power consumption forecasting
Stacked generalization ensemble method
XGBoost
ISSN:2948-2992
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Shrnutí:Abstract Load forecasting is considered to be the core of an efficient predictive energy management for buildings. In this context, the deployment of smart meters and sensors enabled continuous energy usage monitoring in modern buildings. This streaming data led to the development of a new Online Home Energy Management System (OHEMS). The aim of this study is to develop an advanced smart metering infrastructure for online power forecasting, using embedded hardware with low computing power and real time constraints. As a benchmark, we applied both online and offline load forecasting modes to assess three prediction approaches in terms of accuracy and computational time. A single Machine learning algorithm using Long Short-Term Memory (LSTM) and hybrid Machine learning algorithms (CNN-LSTM), and ensembles machine learning approaches including eXtreme Gradient Boosting Machine (XGBoost) and Random Forest (RF). Furthermore, a novel practical stacking method for Short-Term Load Forecasting (STLF) using a stacked generalization ensemble method, which combines XGBoost and RF methods has been proposed. In online mode, the proposed stacking model achieved the best forecasting performance with a sMAPE of 1.15%, followed by RF (1.22%), XGBoost (1.23%), CNN-LSTM (1.97%) and LSTM (2.20%). In offline mode, the CNN-LSTM model outperformed all other methods with a sMAPE of 1.01%, demonstrating the advantage of deep feature extraction and complete data availability in batch forecasting. Performance-based retraining was shown to be more effective than periodic retraining, which might still be useful in fog computing scenarios. In general, offline CNN-LSTM is preferable for scenarios demanding maximum accuracy, while the stacking model is more suitable for scalable, real-time online forecasting in constrained environments.
ISSN:2948-2992
DOI:10.1007/s10791-025-09640-z