Electricity Load & Renewable Forecasting

A comparative analysis of LSTM, GRU, and TCN Models for short-term load forecasting in renewable-rich German power grids.

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Project Overview

As power systems integrate increasing amounts of wind and solar generation, short-term load forecasting is critical for maintaining grid stability and minimizing balancing costs. This project forecasts hourly electricity load in Germany utilizing modern Deep Learning architectures.

By building a multivariate time series that combined historical load with onshore wind, solar generation, and calendar features, we structured a pipeline to evaluate the most resource-efficient layout between Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Temporal Convolutional Networks (TCN).

Methodology & Data Processing

The system was trained off of Open Power System Data (OPSD). Operational data inherently contains noise and gaps, requiring rigorous preparation pipelines:

  • Time-Based Interpolation: Missing values were handled via continuous time-based forward and backward filling to retain temporal validity.
  • Standardization: Scaling features to stabilize model optimization and avoid neural bias toward large values without causing cross-set data leakage.
  • Sliding-Window Sequences: Structured to capture the previous 24 hours of system conditions (load, solar, wind, & calendar features) to predict the next 24 hours chronologically using Keras input shapes.
Deep Learning Architecture Visualization

TCN and GRU Neural Network Flow Representation

Architectural Evaluation

Three models were benchmarked to identify optimal operational deployments:

  • LSTM Baseline: Yielded the lowest configuration stability and underestimated peak loads, serving generally as an architecture foundation.
  • Temporal Convolutional Networks (TCN): Replaced recurrence with stacks of 1D causal dilated convolutions. Exploiting Bayesian Optimization, the TCN delivered strong competitive accuracy and executed up to 3.5× faster.
  • Multivariate GRU (Winner): The 64-unit stacked GRU model seamlessly handled massive predictive deviations and accurately tracked fluctuating drops and ramps over test data.
Key Architectural Finding: Including wind and solar variations produced an improvement of over 16.5% relative to estimating standard load histories, proving standard univariate predictions in modern grids are inadequate.

Results & Impact

463 MW (Mean Absolute Error)
601 MW (Root Mean Squared Error)

The multivariate GRU emerged as the most resilient model overall, whereas the TCN offered a powerful accuracy-efficiency trade-off for time-critical deployments. Such frameworks supply crucial unit commitments for regional grids integrating renewable resources at exponential scales.