Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network

Sameh Mahjoub, Sami Labdai, Larbi Chrifi-Alaoui (), Bruno Marhic and Laurent Delahoche
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Sameh Mahjoub: Laboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne, 80000 Amiens, France
Sami Labdai: Laboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne, 80000 Amiens, France
Larbi Chrifi-Alaoui: Laboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne, 80000 Amiens, France
Bruno Marhic: Laboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne, 80000 Amiens, France
Laurent Delahoche: Laboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne, 80000 Amiens, France

Energies, 2023, vol. 16, issue 4, 1-18

Abstract: In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO 2 , noise, and relative temperature via our machine learning method and forecasting strategy. The proposed algorithms enhance the energy management system through the optimal use of the electric heating system. The Long Short-Term Memory (LSTM) neural network is a special deep learning strategy for processing time series prediction that has shown promising prediction results in recent years. To improve the performance of the LSTM algorithm, particularly for autocorrelation prediction, we will focus on optimizing weight updates using various approaches such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performances of the proposed methods are evaluated using real available datasets. Test results reveal that the GA and the PSO can forecast the parameters with higher prediction fidelity compared to the LSTM networks. Indeed, all experimental predictions reached a range in their correlation coefficients between 99.16% and 99.97%, which proves the efficiency of the proposed approaches.

Keywords: deep neural networks; LSTM; time series prediction; optimisation; GA; PSO (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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