AIMC Topic: Electricity

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Learning from leading indicators to predict long-term dynamics of hourly electricity generation from multiple resources.

Neural networks : the official journal of the International Neural Network Society
Electricity is generated through various resources and then flows between regions via a complex system (grid). Imbalances in electricity generation can lead to the waste of renewable energy. As renewable energy is becoming a larger part of the grid, ...

Electrically Driven, Bioluminescent Compliant Devices for Soft Robotics.

ACS applied materials & interfaces
Soft robotics, a research field wherein robots are fabricated from compliant materials, has sparked widespread research interest because of its potential applications in a variety of scenarios. In soft robots, luminescence is an important functionali...

Optimization of carbon footprint management model of electric power enterprises based on artificial intelligence.

PloS one
This study intends to optimize the carbon footprint management model of power enterprises through artificial intelligence (AI) technology to help the scientific formulation of carbon emission reduction strategies. Firstly, a carbon footprint calculat...

Extended DEMATEL method with intuitionistic fuzzy information: A case of electric vehicles.

PloS one
The Decision-Making Trial and Laboratory (DEMATEL) methodology excels in the analysis of interdependent factors within complex systems, with correlation data typically presented in crisp values. Nevertheless, the judgments made by decision-makers oft...

How peptide migration and fraction bioactivity are modulated by applied electrical current conditions during electromembrane process separation: A comprehensive machine learning-based peptidomic approach.

Food research international (Ottawa, Ont.)
Industrial wastewaters are significant global concerns due to their environmental impact. Yet, protein-rich wastewaters can be valorized by enzymatic hydrolysis to release bioactive peptides. However, achieving selective molecular differentiation and...

Advancing ensemble learning techniques for residential building electricity consumption forecasting: Insight from explainable artificial intelligence.

PloS one
Accurate electricity consumption forecasting in residential buildings has a direct impact on energy efficiency and cost management, making it a critical component of sustainable energy practices. Decision tree-based ensemble learning techniques are p...

Mapping Spatiotemporal Disparities in Residential Electricity Inequality Using Machine Learning.

Environmental science & technology
The move toward electrification is critical for decarbonizing the energy sector but may exacerbate energy unaffordability without proper safeguards. Addressing this challenge requires capturing neighborhood-scale dynamics to uncover the blind spots i...

Cross-domain zero-shot learning for enhanced fault diagnosis in high-voltage circuit breakers.

Neural networks : the official journal of the International Neural Network Society
Ensuring the stability of high-voltage circuit breakers (HVCBs) is crucial for maintaining an uninterrupted supply of electricity. Existing fault diagnosis methods typically rely on extensive labeled datasets, which are challenging to obtain due to t...

Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system.

PloS one
The reliable operation of electrical power transmission systems is crucial for ensuring consumer's stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and ...

Optimizing wave energy converter benchmarking with a fuzzy-based decision-making approach.

PloS one
The quest for sustainable energy solutions has intensified interest in marine renewables, particularly wave energy. This study addresses the crucial need for an objective assessment of Wave Energy Converter (WEC) technologies, which are instrumental ...