A review on artificial intelligence thermal fluids and the integration of energy conservation with blockchain technology

Discover Sustainability, Год журнала: 2025, Номер 6(1)

Опубликована: Апрель 10, 2025

Язык: Английский

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SmartAPM framework for adaptive power management in wearable devices using deep reinforcement learning

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 26, 2025

Wearable devices face a significant challenge in balancing battery life with performance, often leading to frequent recharging and reduced user satisfaction. In this paper, we introduce the SmartAPM (Smart Adaptive Power Management) framework, novel approach that leverages deep reinforcement learning (DRL) optimize power management wearable devices. The key objective of is prolong while enhancing experience through dynamic adjustments specific usage patterns. We compiled comprehensive dataset by integrating activity data, sensor readings, consumption metrics from various sources, including WISDM, UCI HAR, ExtraSensory. Synthetic profiles device specifications were incorporated into enhance training. employs multi-agent framework combines on-device cloud-based techniques, as well transfer learning, personalization. Simulations on demonstrate can extend 36% compared traditional methods, also increasing satisfaction 25%. system adapts new patterns within 24 h utilizes less than 5% device's resources. has potential revolutionize energy devices, inspiring era efficiency

Язык: Английский

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Enhancing electricity theft detection with ADASYN-enhanced machine learning models

Electrical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 23, 2025

Язык: Английский

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Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features

Discover Sustainability, Год журнала: 2024, Номер 5(1)

Опубликована: Дек. 31, 2024

This study evaluates and differentiates five advanced machine learning models—LSTM, GRU, CNN-LSTM, Random Forest, SVR—aimed at precisely estimating solar wind power generation to enhance renewable energy forecasting. LSTM achieved a remarkable Mean Squared Error (MSE) of 0.010 R2 score 0.90, highlighting its proficiency in capturing intricate temporal relationships. GRU closely followed, demonstrating potential as viable option due combination computational efficiency accuracy (MSE = 0.015, 0.88). In datasets abundant spatial correlations, the CNN-LSTM hybrid demonstrated utility by providing novel insights into spatial–temporal patterns; nonetheless, it lagged considerably accuracy, with mean squared error 0.020 0.87. Conversely, traditional models reliable albeit less dynamic ability elucidate complexities data; for instance, Forest exhibited 0.025, while Support Vector Regression (SVR) recorded an MSE 0.030. The results affirm that deep architectures, particularly LSTM, offer transformative method forecasting, hence enhancing reliability management systems.

Язык: Английский

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Optimizing energy systems of livestock farms with computational intelligence for achieving energy autonomy

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 28, 2025

The relevance of the study is due to need increase energy autonomy livestock farms by introducing innovative solutions based on computational intelligence. Given significant consumption farms, as well reduced dependence traditional sources, there a optimise systems using renewable sources. aim research develop model for integrating intelligence achieve their autonomy. use models will allow farmers manage more efficiently, minimise carbon emissions, and overall stability supply. object including subject methods optimisation used resource management. paper develops optimising genetic algorithm that involves systematic implementation 5 steps. In contrast static models, proposed takes into account possibility dynamic adaptation structure supply system real production conditions. This done taking demand external factors such power grid failures weather multi-criteria approach simultaneously reduces CO₂ costs increases sustainability farms. in provides flexible parameter settings search an optimal solution context variable complex system. Based model, Python 3.10 program was created perform labour-intensive calculations According results testing at farm Volyn Nova LLC (Volyn region, Ukraine), it found optimised allows reducing emissions from 1263 kg/day 92.3 increasing Prospects further include other types development integration combined several

Язык: Английский

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Integrating cumulative binomial probability into artificial bee colony algorithm for global optimization in mechanical engineering design

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 151, С. 110628 - 110628

Опубликована: Март 30, 2025

Язык: Английский

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Using Big Data for Predictive Maintenance in Transportation Systems

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 297 - 314

Опубликована: Апрель 4, 2025

The economic and social health of contemporary urban centers is greatly dependent on the transportation industry. Transportation infrastructure must be dependable efficient because any disruptions can have a domino effect mobility as whole. Predictive maintenance, facilitated by analysis big data, gives chance to proactively address maintenance needs minimize service interruptions. use data analytics for predictive in systems examined this chapter. It starts going over special sources that are available industry, such sensor from infrastructure, cars, traffic control systems. explores essential phases procedure, encompassing gathering, analysis, modeling, production practical insights. application data-driven various contexts—such public fleets, road rail networks—is demonstrated through several case studies.

Язык: Английский

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Blockchain based solid waste classification with AI powered tracking and IoT integration

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 30, 2025

Smart waste management is vital for reducing environmental impact and improving quality of life in smart cities. This study presents an AI-driven classification model that integrates IoT Blockchain technologies. IoT-connected bins transmit data to a central server, which uses blockchain ensure secure, transparent storage. AI algorithms, including machine learning (ML) deep (DL), classify real-time, optimizing collection recycling. ensures integrity, while ML DL models enhance sorting efficiency. The system aims improve sustainability through intelligent decision-making secure handling. Performance will be assessed using retrieval metrics visualization tools evaluate the hybrid on detection classification.

Язык: Английский

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SOC estimation for a lithium-ion pouch cell using machine learning under different load profiles

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 24, 2025

Abstract Estimating the state of charge lithium-ion battery systems is important for efficient management systems. This work conducts a thorough evaluation multiple SOC estimate methods, including both classic approaches Coulomb Counting and extended Kalman filter machine learning techniques under different load profile on pouch cell. The assessment included variety experimental data collected from entire cycles, shallow dynamic operations utilizing Worldwide Harmonized Light Vehicles Test Procedure Hybrid Pulse Power Characterization tests done 100% to 10% SOC. While traditional performed well ordinary settings, they had severe limits during cycling. In contrast, technologies, notably random forest method, better across all testing conditions. approach showed outstanding accuracy while minimizing error metrics (RMSE: 0.0229, MSE: 0.0005, MAE: 0.0139) effectively handled typical issues such as drift ageing effects. These findings validate dependable robust real-time estimation in

Язык: Английский

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Artificial Intelligence for Predictive Maintenance and Energy Optimization in Renewables

Springer proceedings in physics, Год журнала: 2025, Номер unknown, С. 656 - 663

Опубликована: Янв. 1, 2025

Язык: Английский

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