Pregled bibliografske jedinice broj: 1254286
Estimation of average wind speed in the city of Tianguá using Artificial Neural Network
Estimation of average wind speed in the city of Tianguá using Artificial Neural Network // 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)
Split, Hrvatska: Institute of Electrical and Electronics Engineers (IEEE), 2021. str. 1-6 doi:10.23919/splitech52315.2021.9566402 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1254286 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Estimation of average wind speed in the city of
Tianguá using Artificial Neural Network
Autori
Ferreira, Arilson F. G. ; de Aragao, Anderson P. ; de L. Veras, Necio ; Rabelo, Ricardo A. L. ; Solic, Petar
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)
/ - : Institute of Electrical and Electronics Engineers (IEEE), 2021, 1-6
Skup
6th International Conference on Smart and Sustainable Technologies (SpliTech 2021)
Mjesto i datum
Split, Hrvatska, 08.09.2021. - 11.09.2021
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Artificial Neural Networks, Multilayer Perceptron, Wind Forecast, Time-Lagged Feedforward Neural Network
Sažetak
This paper presents an Artificial Neural Network (ANN) with Time-Lagged Feedforward Network (TLFN) applied in daily wind speed forecasting. In particular, we used a multilayer perceptron (MLP) with a backpropagation algorithm and the sigmoidal activation function. Wind forecasting is relevant to aid the installation and function of wind farms and agricultural meteorology. We experimented with several MLPs topologies by varying the number of neurons and the number of layers in the ANN and collected wind speed data from a weather station at the Federal Institute of Education, Science and Technology of Ceara (IFCE), Campus Tiangu ´ a (Brazil). The data is included ´ among the years 2014 and 2018. We evaluated each topology according to a Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and the Coefficient of Determination (R²). The results obtained showed the possibility of applying Artificial Neural Networks in daily wind speed forecasting.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Projekti:
UIP-2017-05-4206 - Internet stvari: istraživanja i primjene (IoTRA) (Šolić, Petar, HRZZ - 2017-05) ( CroRIS)
Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split
Profili:
Petar Šolić (autor)