TY - JOUR
T1 - Advancing Electricity Price Forecasting with Conditional Generative Adversarial Networks: A Probabilistic Simulation Framework
AU - Lina Hassan
AU - Omar Khalaf
AU - Reem Jaber
JF - Journal of Smart Grids, Energy Analytics and Optimization
JO - J Smart Grids Energy Anal Optim
Y1 - 2024
VL - 3
IS - 1
SP - 73
N2 - A new method for generating time series simulations of electricity price scenarios is introduced. The “Time Series Simulation Conditional Generative Adversarial Network” (TSS-CGAN) produces short-term scenarios of electricity prices. Specifically, the network can output a 24-dimensional vector that reflects the typical dynamics of electricity markets. This approach can serve as an alternative to conventional financial-mathematical techniques, such as statistical factor models, for modeling the price distribution around a given forecast. The dataset spans 3 years from 2020 to 2023. The empirical analysis focuses on the EPEX SPOT market in Europe. An electricity price scenario consists of the prices for the hourly contracts from a day-ahead auction on the EPEX SPOT power exchange. The model takes multivariate time series as input features, including point forecasts of electricity prices along with fundamental data on generation and load profiles. The TSS-CGAN architecture integrates the principles of Conditional Generative Adversarial Networks with 1D Convolutional Neural Networks and Bidirectional Long Short-Term Memory units. Model performance is assessed using both qualitative and quantitative measures. For evaluation purposes, 10,000 simulations are generated for a test period. Qualitative assessment examines whether the model correctly captures and reproduces key electricity market-specific patterns. The quantitative evaluation involves comparing standard error metrics against benchmark models such as DeepAR, Prophet, and Temporal Fusion Transformer, analyzing quantile ranges and error distributions, and conducting a sensitivity analysis. The findings demonstrate that the TSS-CGAN surpasses benchmark models like DeepAR by lowering the continuous ranked probability score by 50% while effectively accounting for market-specific factors, such as the generation of intermittent renewable energies, and responding appropriately to variations in the relevant variables.
UR - https://isespub.com/e019420367
ER -