Abstract Electricity price has been the focus of electricity market participants from the beginning of electricity markets as the price reflects the prevailing market conditions. Its importance and volatile behavior have motivated many research works in the area of electricity price forecasting. While qualified electricity price forecasting methods have been presented in the literature, prediction of price spikes, as a distinctive and crucial aspect of electricity price time series, has been less researched and still remained as a critical challenge. In this paper, a new deep learning-based framework is proposed for price spike occurrence and value prediction. Three deep generative models, for data generation, rebalanced data clustering, and point value forecasting, are combined in this framework. In the first step, the Rebalancing Variational Autoencoder (RebalVAE) is proposed to rebalance the data using a mixture of discrete and continuous variables. After that, the Clustering Variational Auto-Encoder (ClusVAE) with a new clustering-specific loss term from the mutual information theory is proposed to make an accurate and automatic clustering. Subsequently, a deep diffusion model is applied to each cluster for price value prediction. This model, namely Diffused Predictor Variational Auto Encoder (DiffPredVAE), includes a Transformer Encoder, embedded in both Encoder and Decoder networks of a VAE, and is equipped with a new loss function. Results on two real-world datasets (in five cases) confirm the superiority of the proposed price spike prediction model compared to various other models.
Rastgoo et al. (Sun,) studied this question.