AIMC Topic: Electric Power Supplies

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Short-term power prediction of photovoltaic power stations based on Kepler optimization algorithm and VMD-CNN-LSTM model.

PloS one
This study focuses on the short-term power prediction of photovoltaic power stations, aiming to address the intermittent and fluctuating problems of photovoltaic power generation, in order to improve the prediction accuracy and ensure the stable oper...

High-Throughput Molecular Design of Donors and Non-Fullerene Acceptors for Organic Solar Cells Based on Convolutional Neural Networks.

Journal of chemical information and modeling
Designing novel high-performance donor and acceptor molecules is essential for improving the power conversion efficiency (PCE) of organic solar cells (OSCs). However, conventional experimental methods for developing new materials are often time-consu...

Performance enhancement of a wind driven PMSG using an artificial neural network based nonlinear backstepping controller.

PloS one
With the increasing demand for wind energy in the electric power generation industry, optimizing robust and efficient control strategies is essential for a wind energy conversion system (WECS). In this regard, this study proposes a novel hybrid contr...

Design of a class E inverter with stabilized output power using artificial neural network for applications in biomedical implants.

Scientific reports
This paper presents the design, simulation, and experimental validation of a load-independent class E inverter tailored for biomedical implant applications. The proposed system addresses the challenge in the PID controller of maintaining constant out...

Deep learning-based screening approach for priority pollutants: a case study on retired power battery recycling.

Environmental pollution (Barking, Essex : 1987)
With the rapid increase in the production of retired power batteries, the potential environmental risks during recycling must urgently be identified and assessed. This study presented a novel screening framework for pollutant prioritization utilizing...

Optimizing EV charging stations and power trading with deep learning and path optimization.

PloS one
The rapid growth of electric vehicles (EVs) presents significant challenges for power grids, particularly in managing fluctuating demand and optimizing the placement of charging infrastructure. This study proposes an integrated framework combining de...

Electrical grid-independent machine learning-assisted wearable gait analysis device with triboelectric-electromagnetic hybrid energy harvester.

Biosensors & bioelectronics
In this study, an Electrical grid-independent Machine learning-assisted Wearable device for Gait analysis (EMWG) with a ground reaction force sensor is presented. For gait analysis, a multi-layer perceptron is identified as the optimal model among va...

Predictive hybrid model of a grid-connected photovoltaic system with DC-DC converters under extreme altitude conditions at 3800 meters above sea level.

PloS one
This study aims to develop a predictive hybrid model for a grid-connected PV system with DC-DC optimizers, designed to operate in extreme altitude conditions at 3800 m above sea level. This approach seeks to address the "curse of dimensionality" by r...

Flexible hybrid self-powered piezo-triboelectric nanogenerator based on BTO-PVDF/PDMS nanocomposites for human machine interaction.

Scientific reports
As flexible and wearable electronics play more and more important role in smart watches, smart glass and virtual reality, and the power supply to the wearable electronics have been revealed more attentions for long-term usage and continuous healthy m...

Multi-Granularity Autoformer for long-term deterministic and probabilistic power load forecasting.

Neural networks : the official journal of the International Neural Network Society
Long-term power load forecasting is critical for power system planning but is constrained by intricate temporal patterns. Transformer-based models emphasize modeling long- and short-term dependencies yet encounter limitations from complexity and para...