Research Article
A Short Term Wind Speed Forecasting Model Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Models
@INPROCEEDINGS{10.1007/978-3-030-97027-7_12, author={Yahia Amoura and Ana I. Pereira and Jos\^{e} Lima}, title={A Short Term Wind Speed Forecasting Model Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Models}, proceedings={Sustainable Energy for Smart Cities. Third EAI International Conference, SESC 2021, Virtual Event, November 24--26, 2021, Proceedings}, proceedings_a={SESC}, year={2022}, month={3}, keywords={Artificial Neural Network Adaptive Neuro-Fuzzy Inference System Wind speed Temperature Mean Square Error}, doi={10.1007/978-3-030-97027-7_12} }- Yahia Amoura
Ana I. Pereira
José Lima
Year: 2022
A Short Term Wind Speed Forecasting Model Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Models
SESC
Springer
DOI: 10.1007/978-3-030-97027-7_12
Abstract
Future power systems encourage the use of renewable energy resources, among them wind power is of great interest, but its power output is intermittent in nature which can affect the stability of the power system and increase the risk of blackouts. Therefore, a forecasting model of the wind speed is essential for the optimal operation of a power supply with an important share of wind energy conversion systems. In this paper, two wind speed forecasting models based on multiple meteorological measurements of wind speed and temperature are proposed and compared according to their mean squared error (MSE) value. The first model concerns the artificial intelligence based on neural network (ANN) where several network configurations are proposed to achieve the most suitable structure of the problem, while the other model concerned the Adaptive Neuro-Fuzzy Inference System (ANFIS). To enhance the results accuracy, the invalid input samples are filtered. According to the computational results of the two models, the ANFIS has delivered more accurate outputs characterized by a reduced mean squared error value compared to the ANN-based model.
