Optimization of Load Forecasting in Smartgrid using Artificial Neural Network based NFTOOL and NNTOOL

Abstract

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.

Artificial Neural Network Optimized Load Forecasting of Smartgrid using MATLAB

“…Three layers make up a neural network, an input layer, a hidden layer, and an output layer [39]. Temperature, wind speed, rainfall, humidity, previous load data, and actual load data are among the six inputs employed in this study results optimized ANN design [40]. The neural network is composed of a four-layered feed-forward network with a sigmoid activation function in the hidden layer and a linear output neuron.…”