Prediction of electric power and load forcasting using LSTM technique for EMS
A designed electric load and energy forecasting is proposed for buildings. Two different forecasting models, one for electricity consumption (medium-term load forecasting MTLF) and another for electrical en- ergy (very short-term) are proposed, compared, and interpreted. To feed those prediction mod...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2025/15/epjconf_cistem2024_03005.pdf |
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| _version_ | 1839639461097373696 |
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| author | Moustakim Mohammed Amine Nasser Tamou Elkamoun Najib Essadki Ahmed |
| author_facet | Moustakim Mohammed Amine Nasser Tamou Elkamoun Najib Essadki Ahmed |
| author_sort | Moustakim Mohammed Amine |
| collection | DOAJ |
| description | A designed electric load and energy forecasting is proposed for buildings. Two different forecasting models, one for electricity consumption (medium-term load forecasting MTLF) and another for electrical en- ergy (very short-term) are proposed, compared, and interpreted.
To feed those prediction models, and depending on dataset quality, two available online websites are chosen. Forecasting models are developed by PYTHON programming language, using various libraries dedicated to machine learning projects, such as ’TensorFlow’, ’Kiras’ and ’Scikit-learn’, and others for the visualization of results and data visualization like ’MatPlotLib’ and ’Seaborn’, and many other libraries. Because of the type of prediction based on time series, we used Long Short Term Memory (LSTM-type) neural network models.
The load forecasting model proposed in this study outperformed a previous engineering work on the same dataset. The proposed model achieved a minimal MAPE of 3.74% and a minimal RMSE that was approximately 20% lower than the engineer’s work. This article presents the proposed work and its results.
Simulation results are presented using Google collaboratory the hosted Jupyter Notebook service. |
| format | Article |
| id | doaj-art-ff4d37a1e55e4ed490ca6bcd173fc908 |
| institution | Matheson Library |
| issn | 2100-014X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-ff4d37a1e55e4ed490ca6bcd173fc9082025-07-04T09:32:25ZengEDP SciencesEPJ Web of Conferences2100-014X2025-01-013300300510.1051/epjconf/202533003005epjconf_cistem2024_03005Prediction of electric power and load forcasting using LSTM technique for EMSMoustakim Mohammed Amine0Nasser Tamou1Elkamoun Najib2Essadki Ahmed3ST2I. ENSIAS, Mohammed V UniversityST2I. ENSIAS, Mohammed V UniversitySTIC Laboratory. Faculty of Sciences Chouaib Doukkali UniversityHigh National School of Arts and Crafts (ENSAM), Mohammed V UniversityA designed electric load and energy forecasting is proposed for buildings. Two different forecasting models, one for electricity consumption (medium-term load forecasting MTLF) and another for electrical en- ergy (very short-term) are proposed, compared, and interpreted. To feed those prediction models, and depending on dataset quality, two available online websites are chosen. Forecasting models are developed by PYTHON programming language, using various libraries dedicated to machine learning projects, such as ’TensorFlow’, ’Kiras’ and ’Scikit-learn’, and others for the visualization of results and data visualization like ’MatPlotLib’ and ’Seaborn’, and many other libraries. Because of the type of prediction based on time series, we used Long Short Term Memory (LSTM-type) neural network models. The load forecasting model proposed in this study outperformed a previous engineering work on the same dataset. The proposed model achieved a minimal MAPE of 3.74% and a minimal RMSE that was approximately 20% lower than the engineer’s work. This article presents the proposed work and its results. Simulation results are presented using Google collaboratory the hosted Jupyter Notebook service.https://www.epj-conferences.org/articles/epjconf/pdf/2025/15/epjconf_cistem2024_03005.pdf |
| spellingShingle | Moustakim Mohammed Amine Nasser Tamou Elkamoun Najib Essadki Ahmed Prediction of electric power and load forcasting using LSTM technique for EMS EPJ Web of Conferences |
| title | Prediction of electric power and load forcasting using LSTM technique for EMS |
| title_full | Prediction of electric power and load forcasting using LSTM technique for EMS |
| title_fullStr | Prediction of electric power and load forcasting using LSTM technique for EMS |
| title_full_unstemmed | Prediction of electric power and load forcasting using LSTM technique for EMS |
| title_short | Prediction of electric power and load forcasting using LSTM technique for EMS |
| title_sort | prediction of electric power and load forcasting using lstm technique for ems |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2025/15/epjconf_cistem2024_03005.pdf |
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