Optimal power harvesting under partial shading: Binary Greylag Goose optimization for reconfiguration and Machine learning-Based fault diagnosis in solar PV arrays DOI

S. Saravanan,

R. Senthil Kumar,

P. Balakumar

et al.

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 333, P. 119808 - 119808

Published: April 17, 2025

Language: Английский

Optimizing electric vehicle charging in distribution networks: A dynamic pricing approach using internet of things and Bi-directional LSTM model DOI

P. Balakumar,

Senthil Kumar R,

Vinopraba Thirumavalavan

et al.

Energy, Journal Year: 2024, Volume and Issue: 294, P. 130815 - 130815

Published: Feb. 29, 2024

Language: Английский

Citations

6
A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods DOI Creative Commons

Mahziyar Dostmohammadi,

Mona Zamani Pedram,

Siamak Hoseinzadeh

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 364, P. 121264 - 121264

Published: June 12, 2024

The considerable amount of energy utilized by buildings has led to various environmental challenges that adversely impact human existence. Predicting buildings' usage is commonly acknowledged as encouraging efficiency and enabling well-informed decision-making, ultimately leading decreased consumption. Implementing eco-friendly architectural designs paramount in mitigating consumption, particularly recently constructed structures. This study utilizes clustering analysis on the original dataset capture complex consumption patterns over periods. yields two distinct subsets represent low high an additional subset exclusively encompasses weekends, attributed specific behavior occupants. Ensemble models have become increasingly popular due advancements machine learning techniques. research three discrete algorithms, namely Artificial Neural Network (ANN), K-nearest neighbors (KNN), Decision Trees (DT). In addition, application employs more algorithms bagging boosting: Random Forest (RF), Extreme Gradient Boosting (XGB), (GBT). To augment accuracy predictions, a stacking ensemble methodology employed, wherein forecasts generated many are combined. Given obtained outcomes, thorough examination undertaken, encompassing techniques stacking, bagging, boosting, conduct comprehensive comparative study. It pertinent highlight technique consistently exhibits superior performance relative alternative methodologies across spectrum heterogeneous datasets. Furthermore, using genetic algorithm enables optimization combination base learners, resulting notable enhancement prediction accuracy. After implementing this technique, GA-Stacking demonstrated remarkable Mean Absolute Percentage Error (MAPE) scores. improvement observed was substantial, surpassing 90 percent for all subset-1, subset-2, subset-3, achieved R

Language: Английский

Citations

6
A multi-stage techno-economic model for harnessing flexibility from IoT-enabled appliances and smart charging systems: Developing a competitive local flexibility market using Stackelberg game theory DOI

Shuangfeng Dai,

Seyed Amir Mansouri, Shoujun Huang

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 373, P. 123868 - 123868

Published: July 12, 2024

Language: Английский

Citations

6
Hilbert-Huang Transform and machine learning based electromechanical analysis of induction machine under power quality disturbances DOI Creative Commons

V. Indragandhi,

R. Senthil Kumar,

R. Saranya

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103075 - 103075

Published: Oct. 9, 2024

Language: Английский

Citations

6
Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building DOI
Mohd Herwan Sulaiman, Zuriani Mustaffa

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 76, P. 107139 - 107139

Published: June 21, 2023

Language: Английский

Citations

13
Climatic scenario-based integrated recurrent ensemble model for energy demand forecasting DOI
Ali Akbar Rezazadeh,

Akram Avami,

Mahdieh Soleymani Baghshah

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 310, P. 114103 - 114103

Published: March 19, 2024

Language: Английский

Citations

4
Smart Grid Protection with AI and Cryptographic Security DOI

Dasari Kishan Kumar,

Krishnaiahgari Karthik Reddy,

G. Jaspher W. Kathrine

et al.

Published: June 5, 2024

Language: Английский

Citations

4
Short-Term forecasting of floating photovoltaic power generation using machine learning models DOI Creative Commons
Mohd Herwan Sulaiman, Mohd Shawal Jadin, Zuriani Mustaffa

et al.

Cleaner Energy Systems, Journal Year: 2024, Volume and Issue: 9, P. 100137 - 100137

Published: Aug. 16, 2024

Language: Английский

Citations

4
Advancements in photovoltaic technology: A comprehensive review of recent advances and future prospects DOI Creative Commons

Abdelrahman O. Ali,

Abdelrahman T. Elgohr,

Mostafa H. El-Mahdy

et al.

Energy Conversion and Management X, Journal Year: 2025, Volume and Issue: 26, P. 100952 - 100952

Published: March 4, 2025

Language: Английский

Citations

0
Integrated energy-water systems for community-level flexibility: A hybrid deep Q-network and multi-objective optimization framework DOI

P Y Li,

Chenghong Gu, Xiaotong Cheng

et al.

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 4813 - 4826

Published: April 22, 2025

Language: Английский

Citations

0