Open AccessOptimization of Load Forecasting in Smartgrid using Artificial Neural Network based NFTOOL and NNTOOL
Author(s) -
Sthitprajna Mishra,
Bibhu Prasad Ganthia,
Abel Sridharan,
Perumal Rajakumar,
D. Padmapriya,
S. Kaliappan
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2161/1/012068
Subject(s) - computer science , artificial neural network , matlab , smart grid , reliability (semiconductor) , electric power system , graphical user interface , grid , power system simulation , simulation , reliability engineering , artificial intelligence , power (physics) , operating system , engineering , electrical engineering , physics , geometry , mathematics , quantum mechanics
The motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners for making important decisions on unit commitments, energy trading, system security & reliability and optimal reserve capacity. The objective is to produce a desktop version of personal computer based complete expert system which can be used to forecast the future load of a smart grid. Using MATLAB, we can provide adequate user interfaces in graphical user interfaces. This paper devotes study of load forecasting in smart grid, detailed study of architecture and configuration of Artificial Neural Network(ANN), Mathematical modeling and implementation of ANN using MATLAB and Detailed study of load forecasting using back propagation algorithm.