Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network
In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn&g...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/4/1641 |
| _version_ | 1797621252400087040 |
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| author | Sameh Mahjoub Sami Labdai Larbi Chrifi-Alaoui Bruno Marhic Laurent Delahoche |
| author_facet | Sameh Mahjoub Sami Labdai Larbi Chrifi-Alaoui Bruno Marhic Laurent Delahoche |
| author_sort | Sameh Mahjoub |
| collection | DOAJ |
| description | In this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>, 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. |
| first_indexed | 2024-03-11T08:54:10Z |
| format | Article |
| id | doaj.art-b621ba3d7a8a4bf2ba3901e669f18b75 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T08:54:10Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b621ba3d7a8a4bf2ba3901e669f18b752023-11-16T20:15:43ZengMDPI AGEnergies1996-10732023-02-01164164110.3390/en16041641Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM NetworkSameh Mahjoub0Sami Labdai1Larbi Chrifi-Alaoui2Bruno Marhic3Laurent Delahoche4Laboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne, 80000 Amiens, FranceLaboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne, 80000 Amiens, FranceLaboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne, 80000 Amiens, FranceLaboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne, 80000 Amiens, FranceLaboratory of Innovative Technology (LTI, UR-UPJV 3899), University of Picardie Jules Verne, 80000 Amiens, FranceIn this work, we provide a smart home occupancy prediction technique based on environmental variables such as CO<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>2</mn></msub></semantics></math></inline-formula>, 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.https://www.mdpi.com/1996-1073/16/4/1641deep neural networksLSTMtime series predictionoptimisationGAPSO |
| spellingShingle | Sameh Mahjoub Sami Labdai Larbi Chrifi-Alaoui Bruno Marhic Laurent Delahoche Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network Energies deep neural networks LSTM time series prediction optimisation GA PSO |
| title | Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network |
| title_full | Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network |
| title_fullStr | Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network |
| title_full_unstemmed | Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network |
| title_short | Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network |
| title_sort | short term occupancy forecasting for a smart home using optimized weight updates based on ga and pso algorithms for an lstm network |
| topic | deep neural networks LSTM time series prediction optimisation GA PSO |
| url | https://www.mdpi.com/1996-1073/16/4/1641 |
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