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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Present energy demand and modernization are leading to greater fossil fuelconsumption, which has increased environmental pollution and led to climatechange. Hence to decrease dependency on conventional energy sources, renewableenergy sources are considered. Wind energy is a long-term renewable energyresource but its intermittent nature makes it difficult in harnessing it. Sincewind speed prediction is vital there are different methodologies for wind speedestimation available in the literature. In this work, a new hybrid model isproposed by combining auto-regressive integrated moving average (ARIMA), Kalmanfilter and long short-term memory (LSTM) for estimating wind speed which worksmore accurately than the existing methods proposed in the literature. Fromsimulations, it is observed that the proposed method works with better accuracywhen compared to the existing methods.

A Hybrid Approach using ARIMA, Kalman Filter and LSTM for Accurate Wind Speed Forecasting