Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data
Load corrections with respect to power quality (PQ) after the first pre-estimate of Renewable Energy (RE) power consumption must ensure system-tolerant performance without malfunctions. First, acceptable daily load sequences for the attached equipment are combined and determined according to the RE...
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Taylor & Francis Group
2024-12-01
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| Series: | Systems Science & Control Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2024.2395400 |
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| author | Ladislav Zjavka |
| author_facet | Ladislav Zjavka |
| author_sort | Ladislav Zjavka |
| collection | DOAJ |
| description | Load corrections with respect to power quality (PQ) after the first pre-estimate of Renewable Energy (RE) power consumption must ensure system-tolerant performance without malfunctions. First, acceptable daily load sequences for the attached equipment are combined and determined according to the RE potential and charge states in accommodation to user needs and normal operation. The main motivation is a consequent day-to-day verification of algorithmically scheduled power consumption tasks in the proposed two-stage optimisation according to the system resources and user needs. Statistical artificial intelligence (AI) is employed, as local atmospheric turbulences with terrain obstacles and unexpected user activity result in various operational states in real microsystems. A new unconventional neurocomputing strategy, called Differential Learning (DfL), was applied in the modelling and prediction of the high dynamical PQ parameters in an experimental RE based system according to input-output training data, without an exact specification of its behaviour. The DfL models were compared with recent deep and machine learning techniques. Prediction models were formed after an initial detection of adequate daily training intervals. The AI models are finally tested to process the complete 24-hour forecast series of related input variables used in learning, to estimate the PQ target output at the corresponding times. |
| format | Article |
| id | doaj-art-c0898741f003403999da804cd67ae88e |
| institution | OA Journals |
| issn | 2164-2583 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-c0898741f003403999da804cd67ae88e2025-08-20T02:36:39ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832024-12-0112110.1080/21642583.2024.2395400Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-dataLadislav Zjavka0Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, Ostrava, Czech RepublicLoad corrections with respect to power quality (PQ) after the first pre-estimate of Renewable Energy (RE) power consumption must ensure system-tolerant performance without malfunctions. First, acceptable daily load sequences for the attached equipment are combined and determined according to the RE potential and charge states in accommodation to user needs and normal operation. The main motivation is a consequent day-to-day verification of algorithmically scheduled power consumption tasks in the proposed two-stage optimisation according to the system resources and user needs. Statistical artificial intelligence (AI) is employed, as local atmospheric turbulences with terrain obstacles and unexpected user activity result in various operational states in real microsystems. A new unconventional neurocomputing strategy, called Differential Learning (DfL), was applied in the modelling and prediction of the high dynamical PQ parameters in an experimental RE based system according to input-output training data, without an exact specification of its behaviour. The DfL models were compared with recent deep and machine learning techniques. Prediction models were formed after an initial detection of adequate daily training intervals. The AI models are finally tested to process the complete 24-hour forecast series of related input variables used in learning, to estimate the PQ target output at the corresponding times.https://www.tandfonline.com/doi/10.1080/21642583.2024.2395400Power qualitymicro off-gridbinomial networkdifferential learning deep learningprediction statistics |
| spellingShingle | Ladislav Zjavka Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data Systems Science & Control Engineering Power quality micro off-grid binomial network differential learning deep learning prediction statistics |
| title | Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data |
| title_full | Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data |
| title_fullStr | Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data |
| title_full_unstemmed | Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data |
| title_short | Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data |
| title_sort | power quality validation in micro off grid daily load using modular differential lstm deep and probability statistics models processing nwp data |
| topic | Power quality micro off-grid binomial network differential learning deep learning prediction statistics |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2024.2395400 |
| work_keys_str_mv | AT ladislavzjavka powerqualityvalidationinmicrooffgriddailyloadusingmodulardifferentiallstmdeepandprobabilitystatisticsmodelsprocessingnwpdata |