PatchTST-Hourly-Electricity-Demand-Brazil

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PatchTST-Hourly-Electricity-Demand-Brazil

This model is a fine-tuned version of PatchTST model on the Brazilian Hourly Electricity Demand dataset. It achieves the following results on the evaluation set:

• Loss: 0.1926

Model description

PatchTST is a Transformer-based architecture optimized for univariate and multivariate time series forecasting. It introduces a patching mechanism (inspired by Vision Transformers) to capture local temporal patterns, enhancing both performance and efficiency for long input sequences.

This model was fine-tuned for the task of predicting hourly electricity demand in Brazil, more specifically, the North East region, and demonstrates robust performance over the test set.

Intended uses & limitations

This model is best suited for:

• Forecasting electricity demand at an hourly resolution.
• The model was trained on historical demand data and may not generalize well to future patterns with unseen anomalies (e.g., pandemics, blackouts).
• Exogenous variables (like temperature, holidays, or economic activity) were not included in this training version.

Training and evaluation data

The model was trained using the Hourly-Electricity-Demand-Brazil dataset, which contains hourly energy demand data from 2015 to 2024.

• Input: Historical demand time series in hourly resolution.
• Target: Future electricity demand, predicted for a defined forecasting window.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 128
• eval_batch_size: 128
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Framework versions

• Transformers 4.51.1
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1