Load and Price Forecasting Based on Enhanced Logistic Regression in Smart Grid

Tahir, Aroosa; Khan, Zahoor Ali; Javaid, Nadeem; Hussain, Zeeshan; Rasool, Aimen; Aimal, Syeda

doi:10.1007/978-3-030-12839-5_21

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…• Logistic regression (LR) [49,6,24] is a widely used classification model that explains the relationship of several variables with a dichotomous dependent variable. The independent variables are continuous, dichotomous, discrete, or combination [50].…”

Section: Classification Methodsmentioning

confidence: 99%

Machine Learning Approaches To Predict The Stability of Smart Grid

Satu,

Khan

2024

Preprint

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The smart grid is a modern solution to generate, distribute, and use energy effectively and efficiently. Ensuring the stability of the smart grid is critical to guarantee safe and consistent operation. This study proposes a machine learning model to identify smart grid stability more efficiently. To accomplish this, we collected the electrical grid stability dataset from a public machine learning repository and analyzed the correlation of individual features. Different feature selection and transformation techniques were employed to generate subsequent feature sets. Then, various classification techniques were applied to the primary and its subsets to identify the best machine-learning model for predicting smart grid stability more efficiently. The results indicated that MLP showed the highest accuracy (97.06%) for the Z-score transformed data set using Grid Search Optimization. This study demonstrates the potential of using machine learning to predict smart grid stability accurately and efficiently, providing a critical tool for improving the reliability, efficiency, and sustainability of the energy industry.

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Section: Classification Methodsmentioning

confidence: 99%

Machine Learning Approaches To Predict The Stability of Smart Grid

Satu,

Khan

2024

Preprint

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“…With the help of AI/ML techniques, the analysis of the collected data would be beneficial to estimate the state of the grid network, boost the quality of experience (QoE), deploy dynamic pricing and personalized energy services. Several ML/DL approaches have been applied for smart grid networks, including, but not limited to: CNN for load forecasting [165], LSTM-RNN for photovoltaic power prediction [166], KNN for load and price prediction [167], SAE for detection and classification of transmission line faults [168], SVM for cyberattack detection (covert cyber deception assault) [169], and, lastly, random forests combined with CNN for energy theft detection [170].…”

Section: E Smart Grid 20 1) Motivationmentioning

confidence: 99%

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions

Hafi,

Brik,

Frangoudis

et al. 2024

IEEE Access

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Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. The revolution of 6G networks is driven by massive data availability, moving from centralized and big data towards small and distributed data. This trend has motivated the adoption of distributed and collaborative ML/DL techniques. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique that recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks.INDEX TERMS 6G networks, wireless communication, federated deep learning, split deep learning, split federated learning.

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“…For example, the Support vector machine (SVM) technique is one of the best classification models proposed [65]. The decision tree (DT) learning technique and logistic regression approach, are another simple to develop and understand algorithms and have also been extensively modified to find application power grid systems [66], [67]. The k-nearest neighbours (KNN) system is one of the fastest algorithms in terms of training data set.…”

Section: ) Supervised Learning Technique For Sgmentioning

confidence: 99%

Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect

2022

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Smartgrid is a paradigm that was introduced into the conventional electricity network to enhance the way generation, transmission, and distribution networks interrelate. It involves the use of Information and Communication Technology (ICT) and other solution in fault and intrusion detection, mere monitoring of energy generation, transmission, and distribution. However, on one hand, the actual and earlier smartgrid, do not integrate more advanced features such as automatic decision making, security, scalability, self-healing and awareness, real-time monitoring, cross-layer compatibility, etc. On the other hand, the emergence of the digitalization of the communication infrastructure to support the economic sector which among them are energy generation and distribution grid with Artificial Intelligence (AI) and largescale Machine to Machine (M2M) communication. With the future Massive Internet of Things (MIoT) as one of the pillars of 5G/6G network factory, it is the enabler to support the next generation smart grid by providing the needed platform that integrates, in addition to the communication infrastructure, the AI and IoT support, providing a multitenant system. This paper aim at presenting a comprehensive review of next smart grid research trends and technological background, discuss a futuristic next-generation smart grid driven by artificial intelligence (AI) and leverage by IoT and 5G. In addition, it discusses the challenges of nextgeneration smart-grids as it relate to the integration of AI, IoT and 5G for better smart grid architecture. Also, proffers possible solutions to some of the challenges and standards to support this novel trend. A corresponding future work will dwell on the implementation of the discussed integration of AI, IoT and 5G for next-generation smart grid, using Matlab, NS2/NS3, Open-daylight and Mininet as soft tools and compare with related literature.

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Load and Price Forecasting Based on Enhanced Logistic Regression in Smart Grid

Cited by 10 publications

References 18 publications

Machine Learning Approaches To Predict The Stability of Smart Grid

Machine Learning Approaches To Predict The Stability of Smart Grid

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions

Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect

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