Artificial Neural Networks

Taheri, Javid; Zomaya, Albert Y.

doi:10.1007/0-387-27705-6_5

Cited by 7 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The alternative approach in considering Load Classification in Smart Grids is generally used by the ANN (Artificial Neural Network). These contain a number of interconnected processors for computational models where estimated functions are considered in large numbers as inputs even though there is no proper exact mathematical model for the description of the anomaly [45]. Weights of the inputs are accomplished and revolutionize the inputs by relevant function with subsequent neurons in Sets.…”

Section: Classifications In Loadmentioning

confidence: 99%

Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids

Lydia¹,

Kumar²,

Ramya³

2018

IJET

View full text Add to dashboard Cite

The Optimal bidirectional flow of the electric power and the communicational data between suppliers and consumers are greatly enabled by the Smart Electricity in Grid. Reliable and Feasible micro energy generated due to Dynamic Energy Management (DEM) and the electricity market by consumers and suppliers. The smart grid features ICCM, aims to bring out the power at reduced cost. Powerful and practical DEM relies on load and sustainable production. Smart meters attain the huge data quantity through practical methods and solutions in this real world working. Smart Grids are enhanced by the operations such as data analytics, giving out high performance estimation, Adequate data network management and cloud computing. This paper aims focusthe issuesin big data and challenges experienced by the Dynamic Energy Management signed in Smart Grid. A detail explanation of data processing techniques that are mostly implemented and It also provides a brief description of the most commonly used data processing methods and recommended proposes a upcoming future directional research in thefield.

show abstract

Section: Classifications In Loadmentioning

confidence: 99%