دورية أكاديمية
Digital twin with augmented state extended Kalman filters for forecasting electric power consumption of industrial production systems
| العنوان: | Digital twin with augmented state extended Kalman filters for forecasting electric power consumption of industrial production systems |
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| المؤلفون: | A. Baldassarre, J.-L. Dion, N. Peyret, F. Renaud |
| المصدر: | Heliyon, Vol 10, Iss 6, Pp e27343- (2024) |
| بيانات النشر: | Elsevier, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Science (General) LCC:Social sciences (General) |
| مصطلحات موضوعية: | Digital twin, Electrical energy consumption prediction, Augmented state extended Kalman filter, Parameter identification, Science (General), Q1-390, Social sciences (General), H1-99 |
| الوصف: | The work aims to develop an effective tool based on Digital Twins (DTs) for forecasting electric power consumption of industrial production systems. DTs integrate dynamic models combined with Augmented State Extended Kalman Filters (ASEKFs) in a learning process. The connection with the real counterpart is realized exclusively through non-intrusive sensors. This architecture enables the model development of industrial systems (components, machinery and processes) on which complete knowledge is not available, by identifying the model's unknown parameters through short online training phases and small amounts of real-time raw data. ASEKFs track the unknowns keeping models updated as physical systems evolve. When a forecast is needed, the current estimates of the uncertain parameters are integrated into the dynamic models. These can then be used without ASEKFs to predict the actual energy use of the system under the desired operating conditions, including scenarios that differ from typical functioning. The approach is validated offline with reference to the electricity consumption of an automatic coffee machine, which represents a real test environment and a blueprint to design DTs for other industrial systems. The appliance is observed by measuring the supply voltage and the absorbed current. The accuracy of the results is analyzed and discussed. This method is developed in the context of energy consumption prediction and optimization in the manufacturing industry through refined energy management and planning. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2405-8440 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2405844024033747; https://doaj.org/toc/2405-8440 |
| DOI: | 10.1016/j.heliyon.2024.e27343 |
| URL الوصول: | https://doaj.org/article/76901f253ede4cc4a1285c62428f3659 |
| رقم الأكسشن: | edsdoj.76901f253ede4cc4a1285c62428f3659 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 24058440 |
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| DOI: | 10.1016/j.heliyon.2024.e27343 |