English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Renewable energy sources have gained a lot of importance in today’s power generation. These sources of energy are pollution free and freely available in nature. Wind is the most prominent energy source among the renewable energy sources. Increased wind penetration into the existing power system will create reliability problems for grid operation and management. Wind speed forecasting is an important issue in wind power grid integration as it is chaotic in nature. This paper presents a new State Estimation based Neural Network (SENN) for day ahead (24 hours ahead) wind speed forecasting and its performance has been compared with traditional Back Propagation Neural Network (BPNN). SENN is a non iterative technique, where the weights between input-hidden and hidden-output layers are estimated using a Weighted Least Square State Estimation (WLSSE) approach. It accounts noise associated with input and output data, giving accurate results without any iteration. This method is quite efficient and faster compared to conventional Back Propagation Neural Network (BPNN). Journal of Green Engineering, Vol. 8 3, 263–282. River Publishers doi: 10.13052/jge1904-4720.833 This is an Open Access publication. c © 2018 the Author(s). All rights reserved. 264 D. R. Chandra et al.

State Estimation Based Neural Network in Wind Speed Forecasting: A Non Iterative Approach