Energy modeling of thermal energy storage (TES) using intelligent stream processing system
Thermal energy storage (TES) is the core element of renewable energy system (RES) and can considerably affect its overall efficiency. An effective thermal energy storage (TES) should enhance the stratification by restricting inlet mixing. In this paper, an experimental study is presented to evaluate...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S235248472201455X |
| _version_ | 1797900721312497664 |
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| author | Yogender Pal Chandra Tomas Matuska |
| author_facet | Yogender Pal Chandra Tomas Matuska |
| author_sort | Yogender Pal Chandra |
| collection | DOAJ |
| description | Thermal energy storage (TES) is the core element of renewable energy system (RES) and can considerably affect its overall efficiency. An effective thermal energy storage (TES) should enhance the stratification by restricting inlet mixing. In this paper, an experimental study is presented to evaluate the performance of thermal energy storage (TES). Discharging of the tank was conducted with different inlet flow rates to assess the effect of inlet mixing on thermal stratification. Results are quantified in terms of temperature distribution, MIX, and Richardson number and were visualized to predict the behavior of TES. In addition, the data parsing is done in live mode with ad-hoc built stream-processing data layer. Finally a methodology for time series prediction in the context of TES using high end LSTM network is framed. It was concluded that discharging rate of 800 l/h has the maximum mixing and thus the worst stratification, while prediction efficiency fell well within 5.2% of the error range. |
| first_indexed | 2024-04-10T08:49:24Z |
| format | Article |
| id | doaj.art-ba58f83be6b64648a9e77c88b125e025 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T08:49:24Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-ba58f83be6b64648a9e77c88b125e0252023-02-22T04:31:01ZengElsevierEnergy Reports2352-48472022-11-01813211335Energy modeling of thermal energy storage (TES) using intelligent stream processing systemYogender Pal Chandra0Tomas Matuska1Czech Technical University in Prague, Faculty of Mechanical Engineering, Department of Environmental Engineering, Technicka 4, 166 07, Prague 6, Czech Republic; Czech Technical University in Prague, University Center for Energy Efficient Building, 273 43, Buštěhrad, Czech Republic; Corresponding author at: Czech Technical University in Prague, Faculty of Mechanical Engineering, Department of Environmental Engineering, Technicka 4, 166 07, Prague 6, Czech Republic.Czech Technical University in Prague, University Center for Energy Efficient Building, 273 43, Buštěhrad, Czech RepublicThermal energy storage (TES) is the core element of renewable energy system (RES) and can considerably affect its overall efficiency. An effective thermal energy storage (TES) should enhance the stratification by restricting inlet mixing. In this paper, an experimental study is presented to evaluate the performance of thermal energy storage (TES). Discharging of the tank was conducted with different inlet flow rates to assess the effect of inlet mixing on thermal stratification. Results are quantified in terms of temperature distribution, MIX, and Richardson number and were visualized to predict the behavior of TES. In addition, the data parsing is done in live mode with ad-hoc built stream-processing data layer. Finally a methodology for time series prediction in the context of TES using high end LSTM network is framed. It was concluded that discharging rate of 800 l/h has the maximum mixing and thus the worst stratification, while prediction efficiency fell well within 5.2% of the error range.http://www.sciencedirect.com/science/article/pii/S235248472201455XThermal energy storage (TES)Data streaming with RaspberryPi and PythonRenewable Energy |
| spellingShingle | Yogender Pal Chandra Tomas Matuska Energy modeling of thermal energy storage (TES) using intelligent stream processing system Energy Reports Thermal energy storage (TES) Data streaming with RaspberryPi and Python Renewable Energy |
| title | Energy modeling of thermal energy storage (TES) using intelligent stream processing system |
| title_full | Energy modeling of thermal energy storage (TES) using intelligent stream processing system |
| title_fullStr | Energy modeling of thermal energy storage (TES) using intelligent stream processing system |
| title_full_unstemmed | Energy modeling of thermal energy storage (TES) using intelligent stream processing system |
| title_short | Energy modeling of thermal energy storage (TES) using intelligent stream processing system |
| title_sort | energy modeling of thermal energy storage tes using intelligent stream processing system |
| topic | Thermal energy storage (TES) Data streaming with RaspberryPi and Python Renewable Energy |
| url | http://www.sciencedirect.com/science/article/pii/S235248472201455X |
| work_keys_str_mv | AT yogenderpalchandra energymodelingofthermalenergystoragetesusingintelligentstreamprocessingsystem AT tomasmatuska energymodelingofthermalenergystoragetesusingintelligentstreamprocessingsystem |