Optimizing Hvac Systems: Leveraging Environmental Factors to Reduce Energy Consumption Through Deep Learning Models DOI
Chien-Chih Chen, Chen-Yu Pan, T. Hayashi

et al.

Published: Jan. 1, 2024

The impact of the environment on HVAC (Heating, Ventilation, and Air Conditioning) system energy consumption is an issue that cannot be overlooked in today's context. While has historically been addressed through strategic approaches, control issues remain a current deficiency. Therefore, to explore problems between systems reduce consumption, we propose deep learning process utilizes environmental factors conjunction with systems. This applicable various environments outlines time-series model for future control. Ultimately, experimental results show selection can overall by 14.4%. Different combinations up 33.5%, error only 2.32%. represents significant breakthrough building holds promise achieving net-zero carbon emissions future.

Language: Английский

Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions DOI Creative Commons
Serhii Vladov, Łukasz Ścisło, Валерій Сокуренко

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(13), P. 4246 - 4246

Published: June 29, 2024

The article’s main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy reliability readings. A has been developed that integrates closed loops parameters, which regulated based on filtering method. made achieving almost 100% (0.995 or 99.5%) possible reduced loss function 0.005 (0.5%) after 280 training epochs. An algorithm errors backpropagation loops, parameters It combines increasing validation set controlling overfitting, considering error dynamics, preserves model generalization ability. adaptive rate improves adaptation changes conditions, improving performance. mathematically proven regulating closed-loop integration using method, comparison with traditional filters (median-recursive, recursive median), significantly improve efficiency. Moreover, enables reduction 1st 2nd types: 2.11 times compared median-recursive filter, 2.89 4.18 median filter. achieved results increase readings (up reliability, aircraft efficient safe operations thanks improved methods integration. These advances open up new prospects aviation industry, operational efficiency overall flight safety through advanced processing technologies.

Language: Английский

Citations

20
Demand Response-based Multi-Layer Peer-to-Peer Energy Trading Strategy for Renewable-powered Microgrids with Electric Vehicles DOI
Reza Sepehrzad, Amir Saman Godazi Langeroudi, Ahmed Al‐Durra

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135206 - 135206

Published: Feb. 1, 2025

Language: Английский

Citations

4
A Systematic Review on the Integration of Artificial Intelligence into Energy Management Systems for Electric Vehicles: Recent Advances and Future Perspectives DOI Creative Commons
Paúl Arévalo, Danny Ochoa-Correa, Edisson Villa‐Ávila

et al.

World Electric Vehicle Journal, Journal Year: 2024, Volume and Issue: 15(8), P. 364 - 364

Published: Aug. 13, 2024

This systematic review paper examines the current integration of artificial intelligence into energy management systems for electric vehicles. Using preferred reporting items reviews and meta-analyses (PRISMA) methodology, 46 highly relevant articles were systematically identified from extensive literature research. Recent advancements in intelligence, including machine learning, deep genetic algorithms, have been analyzed their impact on improving vehicle performance, efficiency, range. study highlights significant optimization, route planning, demand forecasting, real-time adaptation to driving conditions through advanced control algorithms. Additionally, this explores intelligence’s role diagnosing faults, predictive maintenance propulsion batteries, personalized experiences based driver preferences environmental factors. Furthermore, addressing security cybersecurity threats vehicles’ is discussed. The findings underscore potential foster innovation efficiency sustainable mobility, emphasizing need further research overcome challenges optimize practical applications.

Language: Английский

Citations

15
Implementation and efficient evaluation of backpropagation network training algorithms in parametric simulations of soil hydraulic conductivity curve DOI

Mostafa Rastgou,

Yong He, Qianjing Jiang

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 636, P. 131302 - 131302

Published: May 9, 2024

Language: Английский

Citations

4
Energy management strategy for electrically-powered hydraulic vehicle based on driving mode recognition DOI
Yanhong Lin,

Benyou Liu,

Tiezhu Zhang

et al.

Energy Sources Part A Recovery Utilization and Environmental Effects, Journal Year: 2025, Volume and Issue: 47(1), P. 2480 - 2503

Published: Jan. 16, 2025

The effectiveness of the energy management strategy directly impacts overall system performance a vehicle, particularly under various driving modes. This paper proposes novel electrically-powered hydraulic vehicle that integrates transmission with an electric powertrain. A rule-based is developed to validate feasibility through steady-state simulation. To enhance performance, Random Forest and gradient boosting tree algorithms are employed for velocity feature dimensionality reduction, while K-means clustering used segment Subsequently, genetic algorithm-optimized backpropagation neural network enables precise online mode recognition, fuzzy controller actively regulates flow in real time. Experimental results indicate GBF-EMS achieves final state charge 78.77%, reducing battery consumption by 16.26% compared RB-EMS, 8.92% RF-EMS 2.52% PMP-EMS. study provides new insights into further development optimization electro-hydraulic power systems.

Language: Английский

Citations

0
FPGA‐Based Realization of Intelligent Escalator Controller Using Artificial Neural Network DOI Creative Commons

Azzad Bader Saeed,

Sabah A. Gitaffa,

Reem I. Dawai

et al.

Journal of Electrical and Computer Engineering, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

In this work, a proposed intelligent controller has been designed and implemented for prototype of four stair‐step escalator. The required task is to manage the supplied power driving motor escalator according applied load on stair‐steps, which represented by number persons standing stair‐steps. must realize following objectives: adaptive consumption power, fast processing, high reliability, low cost, contribution work. backpropagation neural network (BPNN) was used in designing software reasons: learning, capability finding optimal solution. using MATLAB package; it involves three layers, they are input, hidden, output layers; input layer neurons, while hidden 10 neurons. After executing testing controller, observed that mean square error (MSE) value reached 8.68 × −18 , gradient 3.41 −9 there fitting 100% between desired actual outputs, indicates reliability accuracy controller. Finally, downloaded field‐programmable gate array (FPGA) ISE Design Suit software, as known, main characteristics FPGA small size, cost.

Language: Английский

Citations

0
Real-time Analytical Solution to Energy Management for Hybrid Electric Vehicles Using Intelligent Driving Cycle Recognition DOI
Yifan Chen, Liuquan Yang, Chao Yang

et al.

Energy, Journal Year: 2024, Volume and Issue: 307, P. 132643 - 132643

Published: July 29, 2024

Language: Английский

Citations

2
Explainable AI and optimized solar power generation forecasting model based on environmental conditions DOI Creative Commons
Rizk M. Rizk‐Allah,

Lobna M. Abouelmagd,

Ashraf Darwish

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(10), P. e0308002 - e0308002

Published: Oct. 2, 2024

This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power generation. The LSTM component forecasts generation rates based on environmental conditions, while the EO optimizes model’s hyper-parameters through training. XAI-based Local Interpretable Model-independent Explanation (LIME) is adapted identify critical factors that influence accuracy of in smart systems. effectiveness proposed X-LSTM-EO evaluated use five metrics; R-squared (R 2 ), root mean square error (RMSE), coefficient variation (COV), absolute (MAE), efficiency (EC). gains values 0.99, 0.46, 0.35, 0.229, 0.95, for R , RMSE, COV, MAE, EC respectively. results this improve performance original conventional LSTM, where improvement rate is; 148%, 21%, 27%, 20%, 134% compared with other machine learning algorithm such as Decision tree (DT), Linear regression (LR) Gradient Boosting. It was shown worked better than DT LR when were compared. Additionally, PSO employed instead validate outcomes, further demonstrated efficacy optimizer. experimental simulations demonstrate can accurately estimate PV response abrupt changes patterns. Moreover, might assist optimizing operations photovoltaic units. implemented utilizing TensorFlow Keras within Google Collab environment.

Language: Английский

Citations

1
Optimizing Hvac Systems: Leveraging Environmental Factors to Reduce Energy Consumption Through Deep Learning Models DOI
Chien-Chih Chen, Chen-Yu Pan, T. Hayashi

et al.

Published: Jan. 1, 2024

The impact of the environment on HVAC (Heating, Ventilation, and Air Conditioning) system energy consumption is an issue that cannot be overlooked in today's context. While has historically been addressed through strategic approaches, control issues remain a current deficiency. Therefore, to explore problems between systems reduce consumption, we propose deep learning process utilizes environmental factors conjunction with systems. This applicable various environments outlines time-series model for future control. Ultimately, experimental results show selection can overall by 14.4%. Different combinations up 33.5%, error only 2.32%. represents significant breakthrough building holds promise achieving net-zero carbon emissions future.

Language: Английский

Citations

0