Optimizing Smart Grids with Advanced AI Algorithms for Real-time Energy Management DOI Creative Commons

Geraskin Yuri,

Myasar Mundher Adnan, Yerragudipadu Subbarayudu

et al.

E3S Web of Conferences, Journal Year: 2024, Volume and Issue: 581, P. 01015 - 01015

Published: Jan. 1, 2024

Using optimization techniques based on neural networks, this study explores how microgrids might integrate renewable energy sources. Dealing with problems caused by the uncertainty and unpredictability of generation is primary goal. Renewable has been showing encouraging trends, according to data analysis spanning many time periods. From 120 kWh 140 kWh, there was a steady rise 16.67% in solar utilization. Also, an 18.75% rise, from 80 95 use wind power. There 30% 50 65 output biomass energy. Microgrid load utilization shows rising demands commercial, industrial, residential areas. Commercial industrial loads climbed 15% 10%, respectively, while increased 150 165 kWh. With predictions at 98.4%, 95.5%, 97.3%, made using networks were highly congruent actual

Language: Английский

Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models DOI
Ahmed Bensaoud, Jugal Kalita

Ad Hoc Networks, Journal Year: 2025, Volume and Issue: 170, P. 103770 - 103770

Published: Jan. 27, 2025

Language: Английский

Citations

1
1VMD-ATT-LSTM Electricity Price Prediction Based on Grey Wolf Optimization Algorithm in Electricity Markets Considering Renewable Energy DOI
Yuzhen Xu, Xin Huang, Xidong Zheng

et al.

Renewable Energy, Journal Year: 2024, Volume and Issue: unknown, P. 121408 - 121408

Published: Sept. 1, 2024

Language: Английский

Citations

4
Enhancing smart grid reliability with advanced load forecasting using deep learning DOI

J. Jasmine,

M. Germin Nisha,

Rajesh Prasad

et al.

Electrical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Language: Английский

Citations

0
Contextual Background Estimation for Explainable AI in Temperature Prediction DOI Creative Commons

Bartosz Szóstak,

Rafał Doroz,

Michael Märker

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1057 - 1057

Published: Jan. 22, 2025

Accurate weather prediction and electrical load modeling are critical for optimizing energy systems mitigating environmental impacts. This study explores the integration of novel Mean Background Method Estimation with Explainable Artificial Intelligence (XAI) aim to enhance evaluation understanding time-series models in these domains. The or temperature predictions regression-based problems. Some XAI methods, such as SHAP, require using base value model background provide an explanation. However, contextualized situations, default is not always best choice. selection can significantly affect corresponding Shapley values. paper presents two innovative methods designed robust context-aware explanations regression problems, addressing gaps interpretability. They be used improve make more conscious decisions made by that use data.

Language: Английский

Citations

0
A hybrid load forecasting system based on data augmentation and ensemble learning under limited feature availability DOI

Qing Yang,

Zhirui Tian

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125567 - 125567

Published: Oct. 1, 2024

Language: Английский

Citations

3
Advanced mathematical modeling of mitigating security threats in smart grids through deep ensemble model DOI Creative Commons
Sanaa Sharaf, Mahmoud Ragab,

Nasser Albogami

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 4, 2024

Language: Английский

Citations

2
Literature Review of Explainable Tabular Data Analysis DOI Open Access

Helen O’Brien Quinn,

Mohamed Sedky, Janet Francis

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(19), P. 3806 - 3806

Published: Sept. 26, 2024

Explainable artificial intelligence (XAI) is crucial for enhancing transparency and trust in machine learning models, especially tabular data used finance, healthcare, marketing. This paper surveys XAI techniques data, building on] previous work done, specifically a survey of explainable analyzes recent advancements. It categorizes describes methods relevant to identifies domain-specific challenges gaps, examines potential applications trends. Future research directions emphasize clarifying terminology, ensuring security, creating user-centered explanations, improving interaction, developing robust evaluation metrics, advancing adversarial example analysis. contribution aims bolster effective, trustworthy, transparent decision making the field XAI.

Language: Английский

Citations

1
Optimizing Smart Grids with Advanced AI Algorithms for Real-time Energy Management DOI Creative Commons

Geraskin Yuri,

Myasar Mundher Adnan, Yerragudipadu Subbarayudu

et al.

E3S Web of Conferences, Journal Year: 2024, Volume and Issue: 581, P. 01015 - 01015

Published: Jan. 1, 2024

Using optimization techniques based on neural networks, this study explores how microgrids might integrate renewable energy sources. Dealing with problems caused by the uncertainty and unpredictability of generation is primary goal. Renewable has been showing encouraging trends, according to data analysis spanning many time periods. From 120 kWh 140 kWh, there was a steady rise 16.67% in solar utilization. Also, an 18.75% rise, from 80 95 use wind power. There 30% 50 65 output biomass energy. Microgrid load utilization shows rising demands commercial, industrial, residential areas. Commercial industrial loads climbed 15% 10%, respectively, while increased 150 165 kWh. With predictions at 98.4%, 95.5%, 97.3%, made using networks were highly congruent actual

Language: Английский

Citations

0