Active power filter module function to improve power quality conditions using GWO and PSO techniques for solar photovoltaic arrays and battery energy storage systems

Journal of Energy Storage, Год журнала: 2023, Номер 72, С. 108552 - 108552

Опубликована: Авг. 9, 2023

Язык: Английский

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Dynamic pricing for load shifting: Reducing electric vehicle charging impacts on the grid through machine learning-based demand response

Sustainable Cities and Society, Год журнала: 2024, Номер 103, С. 105256 - 105256

Опубликована: Фев. 7, 2024

Язык: Английский

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Low-carbon urban–rural modern energy systems with energy resilience under climate change and extreme events in China—A state-of-the-art review

Energy and Buildings, Год журнала: 2024, Номер 321, С. 114661 - 114661

Опубликована: Авг. 10, 2024

Язык: Английский

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A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

Thermo, Год журнала: 2024, Номер 4(1), С. 100 - 139

Опубликована: Март 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.

Язык: Английский

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Optimal Adaptive Fractional Order Integral Sliding Mode Controller-Energy Management Strategy for Electric Vehicles Based on Bald Eagle Search Algorithm

International Journal of Energy Research, Год журнала: 2024, Номер 2024, С. 1 - 22

Опубликована: Фев. 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.

Язык: Английский

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Optimized LSTM-Based Electric Power Consumption Forecasting for Dynamic Electricity Pricing in Demand Response Scheme of Smart Grid

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104356 - 104356

Опубликована: Фев. 1, 2025

Язык: Английский

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Advancing smart net-zero energy buildings with renewable energy and electrical energy storage

Journal of Energy Storage, Год журнала: 2025, Номер 114, С. 115850 - 115850

Опубликована: Фев. 22, 2025

Язык: Английский

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Supply-demand side management of a building energy system driven by solar and biomass in Stockholm: A smart integration with minimal cost and emission

Energy Conversion and Management, Год журнала: 2023, Номер 292, С. 117420 - 117420

Опубликована: Июль 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.

Язык: Английский

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Wasserstein generative adversarial networks-based photovoltaic uncertainty in a smart home energy management system including battery storage devices

Energy, Год журнала: 2024, Номер 306, С. 132412 - 132412

Опубликована: Июль 14, 2024

Язык: Английский

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Optimizing electric vehicle charging in distribution networks: A dynamic pricing approach using internet of things and Bi-directional LSTM model

Energy, Год журнала: 2024, Номер 294, С. 130815 - 130815

Опубликована: Фев. 29, 2024

Язык: Английский

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