1. ATTnet: An explainable gated recurrent unit neural network for high frequency electricity price forecasting
- Author
-
Haolin Yang and Kristen R. Schell
- Subjects
Deep learning ,RNN ,Time series analysis ,Electricity price forecasting ,Attention mechanism ,SHAP ,Production of electric energy or power. Powerplants. Central stations ,TK1001-1841 - Abstract
The primary contribution of this study is the proposal of an explainable deep-learning neural network (ATTnet) that employs an attention mechanism to achieve accurate electricity spot price forecasting and an explainable model pipeline. The concise, single-stream network consists of a 5-head attention mechanism and gated recurrent units, which have been developed to model the temporal dependencies of the volatile market data. In addition to introducing a novel neural network architecture for volatile time series data, this study makes a substantial contribution by investigating prediction factors in two ways: temporally via the attention scores from the input sequences and globally via feature Shapely values. In real-time electricity price prediction, historical prices, temperature, hour, and zonal load are found to be the most important variables. The deep learning model was tested on real-time price profiles from eight generators within the New York Independent System Operator (NYISO) network. The proposed model achieves performance gains of 21% in MAE and 22% in MAPE over the state-of-the-art benchmark methods.
- Published
- 2024
- Full Text
- View/download PDF