Day-ahead Electricity Price Forecasting by a New Hybrid Algorihtm based on ELM, Curvelet Transform, Preprocessing System, and Modified VCS Algorithm

Given that the price signal in the electricity market is highly volatile or otherwise uncertain, short-term forecasting is significantly affected. Since time-series methods cannot estimate such nonlinear models appropriately with high accuracy, we need to provide an efficient model. For this reason, in this paper, a new hybrid algorithm for day-ahead electricity price forecasting is proposed. In order to achieve this model, we first divide the forecasting problem into three main layers: preprocessor, training, and regulator. In the first layer, we use the curvelet transform to reduce possible noise in the price signal. Then, using the extended data selection model based on increasing correlation and decreasing redundancy, we eliminate the unnecessary data and reduce the volume of computation significantly. Then the regularized data is entered into the learning layer which is a developed Extreme Learning Machine (ELM) to obtain and extract the best pattern from the input data. Since adjusting the control parameters of the proposed ELM can maximize its ability to derive a nonlinear pattern from the price signal, a new developed Virus Colony Search (VCS) method based on the time-varying coefficients theory is proposed in the last layer. The proposed algorithm is a novel optimization method based on the function of viruses to destroy host cells and penetrate the best ones into a cell for replication. The proposed method is applied to existing real electricity markets and the results are compared based on prediction error rates and error-based criteria. The obtained results show the appropriate and acceptable performance of the proposed forecasting method.