Purpose
The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.
Design/methodology/approach
The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.
Findings
The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480.
Originality/value
Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.
Box-Jenkins methodology is one of the most famous modeling approaches to describe the underlying stochastic structure and forecasting future values of various phenomena. In this methodology, the models are of type ARIMA, that is, autoregressive integrated moving average. Some advantages of those include robustness, easiness to use, and wide applicability in various disciplines ranging from engineering to economics. Inflation has been a highly discussed issue in economics. This research focuses on modeling and forecasting the yearly inflation rate of Iran from 1960 to 2019 using ARIMA. According to various measures, different ARIMA models are investigated to confirm their effectiveness. It is here showed that non-seasonal ARIMA (1,0,0) is the most appropriate model for this application.
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