Wind power generation via ground wind station and topographical feedforward neural network (T-FFNN) model for small-scale applications

• Main Authors: Salisu Muhammad, Lawan, Wan Azlan, Wan Zaina Abidinl, Thelaha, Bin Hj Masri, Chai, Wangyin, Baharun, Azhaili
• Format: E-Article
• Language: English
• Published: Elsevier Ltd 2017
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://ir.unimas.my/id/eprint/14963/1/Wind-power-generation-via-ground-wind-station-and-topographical-feedforward-neural-network-%28T-FFNN%29-model-for-small-scale-applications_2017_Journal-of-Cleaner-Production.html; http://ir.unimas.my/id/eprint/14963/; http://www.sciencedirect.com/science/article/pii/S0959652616320194
• Institution: Universiti Malaysia Sarawak

This study presents the potential of harvesting wind energy in Sarawak, Malaysia based on the ground station and prediction models. A topographical feedforward neural network (T-FFNN) is proposed as an alternative to predict the wind speed in the areas where wind speed measurements are not done. The...