Abstract
This paper proposes a novel hybrid forecasting model with three main parts to accurately forecast daily electricity prices. In the first part, where data are divided into high- and low-frequency data using the fractional wavelet transform, the best data with the highest relevancy are selected, using a feature selection algorithm. The second part is based on a nonlinear support vector network and auto-regressive integrated moving average (ARIMA) method for better training the previous values of electricity prices. The third part optimally adjusts the proposed support vector machine parameters with an error-base objective function, using the improved grey wolf and particle swarm optimization. The proposed method is applied to forecast electricity markets, and the results obtained are analyzed with the help of the criteria based on the forecast errors. The results demonstrate the high accuracy in the MAPE index of forecasting the electricity price, which is about 91% as compared to other forecasting methods.
Highlights
Electrical energy, as a source of human activities, is of vital importance to human life, and, countries all over the world are seeking access to a reliable power supply [1,2,3,4,5,6,7,8,9,10,11,12]
The proposed method in this study shows that a meta-training system built on 65 load data presents a forecasting task which will significantly reduce the error in comparison with other existing algorithms
instruction vector (In) order to compare the efficiency of the prediction methods, criteria, such as the mean absolute percent error (MAPE), mean absolute error (MAE), and daily mean absolute percent error (DMAPE) are used, which are defined by the following relations:
Summary
Electrical energy, as a source of human activities, is of vital importance to human life, and, countries all over the world are seeking access to a reliable power supply [1,2,3,4,5,6,7,8,9,10,11,12]. One of the most important arguments for prediction is the proper use of input data to find the best possible relationship between them and, expect a forecast Another feature of this method is reducing the computational time of the program, which plays a vital role for meeting the criteria of accuracy. Due to the lack of a proper solution for capturing the relevant information on the forecasting load scenarios, in this paper, the modified feature selection algorithm based on the maximum relevancy and minimum redundancy is employed to sort the data for the best possible options with the highest correlation for training the least squares support vector machine. By applying the proposed wavelet transform, the data can be divided into several separate sections, each of which can be justified via the time basis This operation increases the ability of the support vector machine to learn and train.
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