notesum.ai

Published at October 20

Power Plays: Unleashing Machine Learning Magic in Smart Grids

cs.AI

cs.LG

Released Date: October 20, 2024

Authors: Abdur Rashid¹, Parag Biswas¹, abdullah al masum¹, MD Abdullah Al Nasim², Kishor Datta Gupta³

Aff.: ¹MSEM Department, Westcliff University, California, United States; ²Research and Development Department, Pioneer Alpha, Dhaka, Bangladesh; ³Department of Computer and Information Science, Clark Atlanta University, Georgia, USA

Arxiv: https://arxiv.org/abs/2410.15423v1

ML Technique	Future Scopes in Smart Grids
Support Vector Machine (SVM)	1. Enhancing anomaly detection and fault diagnosis through improved robustness and scalability. 2. Integrating with hybrid models for better prediction accuracy in complex environments. 3. Developing online SVM algorithms for adaptive learning in dynamic conditions.
Random Forest	1. Expanding use for multi-class classification, such as categorizing grid disturbances. 2. Incorporating in predictive maintenance for better equipment failure prediction. 3. Enhancing interpretability to aid decision-making by grid operators.
K-Nearest Neighbors (KNN)	1. Developing efficient algorithms to handle large-scale smart grid data. 2. Applying for demand response optimization by clustering customers. 3. Integrating with other ML techniques to improve adaptability to non-linear data.
Gradient Boosting	1. Improving scalability for handling large smart grid data. 2. Utilizing for accurate load forecasting and energy pricing models. 3. Investigating use in hybrid models for robust prediction.
Decision Trees	1. Exploring advanced pruning techniques to enhance efficiency and accuracy. 2. Combining with ensemble methods to improve predictive performance. 3. Developing real-time models for adaptive learning in dynamic environments.
Naive Bayes	1. Expanding use for probabilistic forecasting in energy markets. 2. Integrating with other models to improve robustness in non-linear data. 3. Adapting for real-time anomaly detection in grid networks.
Linear Regression	1. Enhancing with feature selection techniques for better accuracy. 2. Applying for long-term trend analysis and energy demand forecasting. 3. Integrating with other techniques to model complex relationships.
Principal Component Analysis (PCA)	1. Extending use for dimensionality reduction in high-dimensional data. 2. Integrating with other ML techniques for enhanced feature extraction. 3. Using to identify and remove noise in smart grid datasets.
Bagging	1. Improving reliability and accuracy of fault detection systems. 2. Combining with other ensemble methods for robust hybrid models. 3. Investigating use in distributed computing for large-scale data.
Boosting	1. Enhancing scalability for real-time smart grid applications. 2. Improving predictive maintenance by focusing on hard-to-predict cases. 3. Integrating with deep learning for powerful hybrid models.