2012/12/11 by Ernesto Cortés Pérez, Ignacio Algredo‐Badillo, Pérez, Ernesto Cortés +4
Computer Science · Decision Sciences · Engineering · Environmental Science · #Artificial Intelligence (cs.AI) #Energy Load and Power Forecasting #FOS: Computer and information sciences #Hydrological Forecasting Using AI #Stock Market Forecasting Methods #cs.AI
paper · pdf · doi:10.48550/arxiv.1212.2671
9 pages, 11 figures, 1 table; IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No 3, September 2012. ISSN (Online): 1694-0814. www.IJCSI.org
arxiv created 2012/12/11 · openalex publication_date 2012/12/11 · arxiv updated 2012/12/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Results are presented on the performance of Adaptive Neuro-Fuzzy Inference system (ANFIS) for wind velocity forecasts in the Isthmus of Tehuantepec region in the state of Oaxaca, Mexico. The data bank was provided by the meteorological station located at the University of Isthmus, Tehuantepec campus, and this data bank covers the period from 2008 to 2011. Three data models were constructed to carry out 16, 24 and 48 hours forecasts using the following variables: wind velocity, temperature, barometric pressure, and date. The performance measure for the three models is the mean standard error (MSE). In this work, performance analysis in short-term prediction is presented, because it is essential in order to define an adequate wind speed model for eolian parks, where a right planning provide economic benefits.