Probability density function forecasting of electricity price: Deep gabor convolutional mixture network
Mousa Afrasiabi, Jamshid Aghaei, Shahabodin Afrasiabi, Mohammad Mohammadi
Abstract
This paper establishes a framework for probabilistic electricity price forecasting in the electrical markets using an end-to-end and direct approach. In the proposed method, firstly, a deep Gabor filter-oriented convolutional network is designed as the strong deep structure. Then, the designed deep Gabor network is developed as a deep mixture network to predict a set of probability density functions (PDFs) in the next hours. To do so, a probabilistic loss function is formulated. The results are validated by the actual electricity market data in California independent system operator (CAISO) and show high level of accuracy and reliability in comparison with other state-of-the-arts methods