Renewable Grid Load Forecaster
Overview
This model is a specialized Informer transformer designed for long-sequence time-series forecasting in smart energy grids. It predicts future electricity demand by integrating historical load data with meteorological variables (solar irradiance, wind speed), specifically optimized for grids with high renewable energy penetration.
Model Architecture
The model implements the Informer architecture to solve the $O(L^2)$ complexity problem of standard Transformers:
- ProbSparse Self-Attention: Reduces complexity to $O(L \log L)$ by selecting the most dominant attention queries.
- Self-Attention Distilling: Compresses the temporal resolution across layers to capture dominant seasonal patterns.
- Generative Decoder: Produces long-range predictions in a single forward pass to avoid cumulative error.
- Mean Absolute Scaled Error (MASE) is used for evaluation:
$$MASE = \frac{\frac{1}{J} \sum_{j=1}^{J} |e_j|}{\frac{1}{T-1} \sum_{t=2}^{T} |Y_t - Y_{t-1}|}$$
Intended Use
- Grid Balancing: Assisting operators in scheduling peaker plants based on predicted renewable shortfalls.
- Battery Management: Optimizing the charge/discharge cycles of utility-scale storage systems.
- Demand Response: Providing data for automated load-shedding programs during peak hours.
Limitations
- Extreme Weather: May under-predict demand during unprecedented "black swan" weather events not present in historical training data.
- Data Dependency: Requires high-fidelity telemetry from both the grid and localized weather stations to maintain accuracy.
- Latency: While faster than traditional Transformers, it requires significant GPU memory for very long context windows (>2000 steps).
