Firstly an ELM is utilized to forecast the short-term wind power.
The wavelet analysis was utilized to capture multiscale data characteristics, while a real neural network (RNN) was utilized to forecast crude oil prices at different scales.
It is found that for developing and developed economies, forecasted EEC trends are significantly different, as expected, and IPSO ANN model can be utilized to forecast long-term EEC up to 2030 with mean absolute percentage error of 1.94 and 1.51% for Iran and the U.S., respectively.
In the present study, Singular Spectrum Analysis (SSA) is utilized to forecast the daily rainfall time series pertaining to Koyna watershed in Maharashtra, India, for 365 days after extracting various components of the rainfall time series such as trend, periodic component, noise and cyclic component.
Subsequently, this model is utilized to forecast the real pattern of genes' differential expression.
In this study, four methods of forecasting using artificial intelligence (artificial neural networks with radial basis function, adaptive neuro-fuzzy inference system, artificial neural network-genetic algorithm hybrid and artificial neural network-particle swarm optimization) are utilized to accurately forecast short-term wind speed data for Tehran, Iran.
The models are utilized to model and to forecast the daily returns of crude oil prices.
References [30, 31] extract and analyze the load pattern based on the clustering results, while spectral clustering and functional clustering are utilized to prepare for load forecasting in [32, 33].
In this model, an artificial neural network (ANN), an adaptive neuro-fuzzy inference system and an autoregressive moving average (ARMA) are utilized to generate three independent price forecasts.
