IoT-Enhanced Machine Learning for Intelligent Energy Optimization and Predictive Management DOI Open Access

D. Rajalakshmi,

K. Sudharson,

Akhil Nair R.

et al.

International Journal of Electronics and Communication Engineering, Journal Year: 2024, Volume and Issue: 11(11), P. 168 - 178

Published: Nov. 30, 2024

IoT and machine learning systems are changing the energy management landscape since they make it possible to understand analyze data with great detail. In this work, we develop EnerSense, a novel architecture that integrates functionalities for smart meter extraction state-of-the-art Machine Learning techniques manage consumption project load. This model is based on hybrid model, Random Forest AutoRegressive Integrated Moving Average (RF-ARIMA), has an accuracy of 96% in determining behavior investigating outliers. Our framework enables wireless integration real-time tracking effective while reducing cost regimes. Substantial empirical tests show about 20% wastage reduction, proving system can further improve efficiency. solution utility companies be equipped meaningful usage strategies, presenting cost-effective structure optimizes resource use by meeting needs promptly enhancing systems.

Language: Английский

A systematic review of big data innovations in smart grids DOI Creative Commons
Hamed Taherdoost

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102132 - 102132

Published: April 21, 2024

Multiple industries have been revolutionized by the incorporation of data science advancements into intelligent environment technologies, specifically in context smart grids. Smart grids offer a dynamic and efficient framework for management optimization electricity generation, distribution, consumption, thanks to developments big analytics. This review delves integration Grid applications Big Data analytics reviewing 25 papers screened with PRISMA standard. The paper matter encompasses critical domains including adaptive energy management, canonical correlation analysis, novel methodologies blockchain machine learning. emphasizes contributions efficiency, security, sustainability means rigorous methodology.

Language: Английский

Citations

22
A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions DOI Creative Commons
Ibrahim Alhamrouni, Nor Hidayah Abdul Kahar,

Mohaned Salem

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(14), P. 6214 - 6214

Published: July 17, 2024

This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection. As global energy demands increase renewable sources become more integrated, maintaining stability reliability both conventional systems smart grids is crucial. Traditional methods are increasingly insufficient for handling today’s grids’ complex, dynamic nature. paper discusses adoption advanced intelligence methods, including artificial (AI), deep learning (DL), machine (ML), metaheuristic optimization algorithms, other AI such as fuzzy logic, reinforcement learning, model predictive control address these challenges. It underscores critical importance system new challenges integrating diverse sources. The reviews various used in analysis, emphasizing their roles maintenance, fault detection, real-time monitoring. details extensive research on capabilities ML algorithms precision efficiency protection systems, showing effectiveness accurately identifying resolving faults. Additionally, it explores potential logic decision-making under uncertainty, integration IoT big data analytics monitoring optimization. Case studies from literature presented, offering valuable insights into practical applications. concludes by current limitations suggesting areas future research, highlighting need robust, flexible, scalable sector. a resource researchers, engineers, policymakers, providing detailed understanding

Language: Английский

Citations

21
Assessment of the Level of Digitalization of Polish Enterprises in the Context of the Fourth Industrial Revolution DOI Open Access
Dominik Kowal,

Małgorzata Radzik,

Lucia Domaracká

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(13), P. 5718 - 5718

Published: July 4, 2024

Due to the dynamic development of Fourth Industrial Revolution, also known as Industry 4.0, impact coronavirus pandemic on operation enterprises, and increasing demands customers, more companies have taken continue take action increase level digitalization. The implementation innovative solutions contributes sustainability enterprises in various areas (economic, environmental, social), streamlining processes effectiveness, efficiency, quality work. Such activities contribute effective use new opportunities by strengthen their competitiveness market position. digital technologies increases capacity innovate grow, which brings significant benefits terms efficiency competitiveness. authors attempted analyze assess transformation Poland. This study aimed review current state digitization companies, made it possible diagnose maturity Polish identify that will determine quickly or improve internal processes. Qualitative comparable methods were used analysis. results show degree is increasing, and, particular, was influenced COVID-19 pandemic. Nearly half analyzed declared they are budget for presented has cognitive value regarding assessment enterprises. Both managers decision-makers can benefit from because decision-making SMEs crucial effectiveness industrial strategy.

Language: Английский

Citations

3
Artificial intelligence techniques framework in the design and optimisation phase of the doubly fed induction generator's power electronic converters: A review of current status and future trends DOI Creative Commons

Ramesh Kumar Behara,

Akshay Kumar Saha

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 215, P. 115573 - 115573

Published: March 5, 2025

Language: Английский

Citations

0
An online learning method for assessing smart grid stability under dynamic perturbations DOI Creative Commons
Alaa Alaerjan, Randa Jabeur Ben Chikha

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 27, 2025

The increasing complexity of smart grid (SG) systems necessitates advanced methodologies to ensure their stability and reliability. In this work, we propose a novel online learning framework that leverages the Bee Algorithm for Ensemble Learning (BAEL) with dynamic perturbations enhance adaptability performance ML models in SG prediction. key contributions our approach are twofold. First, introduce mechanism systematically adjusts variations within Algorithm, effectively balancing global exploration speed local convergence accuracy throughout process. Second, integrate BAEL strategy, where model selection evolution guided by performance-driven ensemble learning, allowing continuous adaptation evolving data patterns. Through iterative cycles augmented incremental perturbation adjustments, method significantly improves predictive accuracy. To evaluate effectiveness approach, conduct extensive experimental assessments, demonstrating process achieves an F1-score close 100 percent. Additionally, perform comparative analysis between benchmark fusion incorporating Random Forest (RF), Gradient Boosting (GB), eXtreme (XGB) classifiers, under identical conditions, including presence perturbations. results confirm BAEL-based consistently outperforms both these classifiers each them operating independently across all evaluation metrics, highlighting its robustness predicting

Language: Английский

Citations

0
Technological Innovation and Sustainable Transitions DOI
Zaheer Allam, Ali Cheshmehzangi

Published: Jan. 1, 2024

Language: Английский

Citations

1
Smart Community: The new integration of information technology and community governance - Based on the knowledge graph analysis of foreign academic papers DOI Creative Commons

Lihua Ma,

Yifan Li,

Huizhe Yan

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 161866 - 161883

Published: Jan. 1, 2024

With the rapid development of information technology (IT), especially after wide application 5G and Internet Things (IoT) technologies, community management has begun to actively explore integration IT. In this study, we analyzed 717 English articles on topic "smart community" in Web Science database by combining bibliometrics traditional review methods. Using Citespace software, explored literature terms co-citation, keyword co-occurrence, clustering, author-institution cooperation identify eight key areas smart research, established a comprehensive research framework accordingly. Based framework, paper further provides analysis themes hot issues. The study shows that current focus communities is mainly how effectively integrate technologies into governance. IoT AI will more civic engagement, social innovation, healthy digital health, as well economy entrepreneurial ecology. These are expected be potential points for future research.

Language: Английский

Citations

0
Towards Optimization of Green Hydrogen Production: An Analytical and Experimental Investigation of Photovoltaic-Electrolysis Configurations DOI

Hamza Al Nawafah,

Ryoichi S. Amano

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

Citations

0
IoT-Enhanced Machine Learning for Intelligent Energy Optimization and Predictive Management DOI Open Access

D. Rajalakshmi,

K. Sudharson,

Akhil Nair R.

et al.

International Journal of Electronics and Communication Engineering, Journal Year: 2024, Volume and Issue: 11(11), P. 168 - 178

Published: Nov. 30, 2024

IoT and machine learning systems are changing the energy management landscape since they make it possible to understand analyze data with great detail. In this work, we develop EnerSense, a novel architecture that integrates functionalities for smart meter extraction state-of-the-art Machine Learning techniques manage consumption project load. This model is based on hybrid model, Random Forest AutoRegressive Integrated Moving Average (RF-ARIMA), has an accuracy of 96% in determining behavior investigating outliers. Our framework enables wireless integration real-time tracking effective while reducing cost regimes. Substantial empirical tests show about 20% wastage reduction, proving system can further improve efficiency. solution utility companies be equipped meaningful usage strategies, presenting cost-effective structure optimizes resource use by meeting needs promptly enhancing systems.

Language: Английский

Citations

0