Analysis of classical and machine learning based short-term and mid-term load forecasting for smart grid

Rai, Sneha; De, Mala

doi:10.1080/14786451.2021.1873339

Cited by 36 publications

(16 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The introduction of VPP and microgrids, which are cloud-based, proffer an effective and robust solution for the ever-growing alternative energy sources. However, in short/midtem, fault monitoring and detection in VPP and microgrids continue to raise serious concerns [141].…”

Section: ) Real Time Faults Monitoring and Detectionmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

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.

show abstract

Section: ) Real Time Faults Monitoring and Detectionmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

show abstract

“…Rai, S. and De, M. [18] presented their analysis of different ML-based forecast models relating to S.G. The work is dedicated to the N.I.T.…”

Section: Traditional Modelsmentioning

confidence: 99%

Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment

Azeem

Ismail

Jameel

et al. 2022

Sensors

View full text Add to dashboard Cite

Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models.

show abstract

“…Liu et al [87] also provided a neural network-based model with particle swarm optimization (PSO) and showed the feasibility and validity of the model. Rai and De [88] improved a support vector regression model for MTLF with an average minimum mean absolute percentage error (MAPE) of 3.60. Gul et al [89] provide a solution based on CNN and LSTM methods.…”

Section: Mid-term Load Forecastingmentioning

confidence: 99%

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

View full text Add to dashboard Cite

The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

show abstract

Analysis of classical and machine learning based short-term and mid-term load forecasting for smart grid

Cited by 36 publications

References 38 publications

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

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

Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment

Artificial Intelligence Techniques in Smart Grid: A Survey

Contact Info

Product

Resources

About