Published: Jan. 1, 2024
Language: Английский
Published: Jan. 1, 2024
Language: Английский
Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 103, P. 105256 - 105256
Published: Feb. 7, 2024
Language: Английский
Citations
17Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 72, P. 108552 - 108552
Published: Aug. 9, 2023
Language: Английский
Citations
25Energy and Buildings, Journal Year: 2024, Volume and Issue: 321, P. 114661 - 114661
Published: Aug. 10, 2024
Language: Английский
Citations
14International Journal of Energy Research, Journal Year: 2024, Volume and Issue: 2024, P. 1 - 22
Published: Feb. 20, 2024
This research presents an optimal energy management system (EMS) for a lithium-ion battery-supercapacitor hybrid storage used to power electric vehicle. The systems are connected in parallel the DC bus by bidirectional DC-DC converters and feed synchronous reluctance motor through inverter. proposed strategy is built on idea take full benefits of two combined methods: bald eagle search algorithm fractional order integral sliding mode control. To evaluate effectiveness suggested strategy, urban dynamometer driving schedule (UDDS) cycle considered. obtained results compared classical control-based terms voltage ripples, overshoots, battery final state charge. ultimate approve ability enhance quality consumption at same time. Comprehensive processor-in-the-loop (PIL) cosimulations were conducted vehicle using C2000 launchxl-f28379d digital signal processing (DSP) board assess practicability EMS.
Language: Английский
Citations
9Thermo, Journal Year: 2024, Volume and Issue: 4(1), P. 100 - 139
Published: March 6, 2024
Given the climate change in recent decades and ever-increasing energy consumption building sector, research is widely focused on green revolution ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate optimize performance, as shown by plethora studies. Accordingly, paper provides review more than 70 articles from years, i.e., mostly 2018 2023, about applications machine/deep learning (ML/DL) forecasting performance buildings their simulation/control/optimization. This was conducted using SCOPUS database with keywords “buildings”, “energy”, “machine learning” “deep selecting papers addressing following applications: design/retrofit optimization, prediction, control/management heating/cooling systems renewable source systems, and/or fault detection. Notably, discusses main differences between ML DL techniques, showing examples use The aim group most frequent ML/DL techniques used field highlighting potentiality limitations each one, both fundamental aspects for future approaches considered are decision trees/random forest, naive Bayes, support vector machines, Kriging method neural networks. investigated convolutional recursive networks, long short-term memory gated recurrent units. Firstly, various explained divided based methodology. Secondly, grouping aforementioned occurs. It emerges that efficiency issues while management systems.
Language: Английский
Citations
9Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 114, P. 115850 - 115850
Published: Feb. 22, 2025
Language: Английский
Citations
1Energy, Journal Year: 2024, Volume and Issue: 294, P. 130815 - 130815
Published: Feb. 29, 2024
Language: Английский
Citations
6Energy, Journal Year: 2024, Volume and Issue: 306, P. 132412 - 132412
Published: July 14, 2024
Language: Английский
Citations
6Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103075 - 103075
Published: Oct. 9, 2024
Language: Английский
Citations
6Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 292, P. 117420 - 117420
Published: July 20, 2023
As part of the transition to a sustainable future, energy-efficient buildings are needed secure users' comfort and lower built environment's energy footprint associated emissions. This article presents novel, realistic affordable solution minimize smart building systems enable higher renewable use in sector. For this, an intelligent system is being developed using rule-based automation approach that considers thermal comfort, prices, meteorological data, primary use. In order installation cost environmental footprint, batteries not used, heat pump's size decreased via component integration. Also, different resources effectively hybridized photovoltaic panels innovative biomass heater increase share energy, enhance reliability, shave peak load. feasibility, suggested framework assessed from techno-economic standpoints for 100 residential apartments Stockholm, Sweden. Our results show 70.8 MWh electricity transferred local grid, remaining 111.5 used supply building's needs power electrically-driven components. The meets more than 65% space heating demand, mainly at low solar high illustrating value integration strategies reduce system's dependability on grid. further reveal most purchases during cloudy days nights repaid through sale excess production warmer hours, with bidirectional connection monthly less 140 $/MWh years. can be held due exclusion minimizing pump size. proposed has emission index 11.9 kgCO2/MWh carbon dioxide emissions by 70 TCO2/year compared Swedish mix.
Language: Английский
Citations
16