Power Consumption prediction

Overview

This project aims to predict future power consumption using advanced machine learning models such as LSTM, GRU, and Transformer. Accurate power consumption forecasts can improve energy management, reduce operating costs, and improve grid stability in various zones. The dataset contains 52,416 observations collected over 10-minute intervals, and each observation has 9 feature columns describing energy usage and relevant factors. Additional info on the dataset can be found HERE.

Date of creation: August, 2024

Model Selection

• LSTM (Long Short-Term Memory): LSTM models are well-suited for long-term dependencies in time series data by mitigating the vanishing gradient problem. They excel in capturing both short and long-term trends.
• GRU (Gated Recurrent Units): GRUs are a simpler alternative to LSTM, with fewer parameters and faster training times. They balance the performance and complexity trade-off in time series forecasting.
• Transformer: With its self-attention mechanism, the Transformer model is highly effective in modeling long-range dependencies in time series data. It scales well with large datasets and can learn complex temporal relationships.

Quickstart

1. Clone the repository:

   git clone https://github.com/AStroCvijo/Power_Consumption_Prediction

2. Download the Electric Power Consumption dataset, extract it, and paste the .csv file into the Power_Consumption_Prediction/data directory.

3. Navigate to the project directory:

   cd Power_Consumption_Prediction

4. Create a virtual environment:

   python -m venv venv

5. Activate the virtual environment:

   • Linux/macOS:

     source venv/bin/activate

   • Windows:

     venv\Scripts\activate

6. Install the required packages:

   pip install -r requirements.txt

7. Train the model using the default settings:

   python main.py --train

Arguments guide

Training arguments

-t or --train specify you want to train the model
-e or --epochs number of epochs in training
-lr or --learning_rate learning rate in training

Data arguments

-sl or --sequence_length length of the sequences extracted from the data
-ps or --prediction_step how far in the future to predict (1 = 10min, 10 = 100min)
-pt or --prediction_target which of the three zones' power consumption to predict: PowerConsumption_Zone1, PowerConsumption_Zone2, or PowerConsumption_Zone3

Model arguments

-m or --model followed by the model you want to use: LSTM, GRU, or Transformer
-mn or --model_name followed by the name of the model you want to use
-l or --load followed by the path to the model you want to load

LSTM and GRU specific arguments

-hs or --hidden_size size of the hidden layer in the LSTM or GRU models
-nl or --number_of_layers number of layers in the LSTM or GRU models

Transformer specific arguments

-md or --model_dimensions dimensions of the Transformer model
-ah or --attention_heads number of attention heads in the Transformer model

How to Use

Training Example:

python main.py --train --model LSTM --epochs 10 --learning_rate 0.001 --sequence_length 60 --prediction_step 10 --prediction_target PowerConsumption_Zone3 --hidden_size 100 --number_of_layers 3

Loading a Pre-Trained Model example

python main.py --load pretrained_models/LSTM_model.pth --model LSTM --sequence_length 60 --prediction_step 10 --prediction_target PowerConsumption_Zone3 --hidden_size 100 --number_of_layers 3

Model Performance

The models were evaluated based on the following metrics:

• Test Loss: Indicates how well the model performs on unseen data.
• Mean Squared Error (MSE): Punishes larger errors by squaring the differences.
• Mean Absolute Error (MAE): Measures the average magnitude of errors in predictions.

Model	Test Loss	MSE	MAE
LSTM	0.0009	0.0011	0.1735
GRU	0.0012	0.0009	0.1718
Transformer	0.0136	0.0138	0.1934

LSTM (Long Short-Term Memory)

The LSTM model performed the best, achieving the lowest test loss (0.0009) and a competitive MAE (0.0011). This suggests that LSTM effectively captured both short and long-term dependencies, leading to accurate predictions of power consumption.

GRU (Gated Recurrent Units)

GRU also showed strong performance, with a slightly higher test loss (0.0012) than LSTM but the lowest MAE (0.0009) and MSE (0.1718). This indicates GRU is highly efficient in reducing prediction errors and is a strong alternative to LSTM.

Transformer

The Transformer model struggled with this task, showing a significantly higher test loss (0.0136), MAE (0.0138), and MSE (0.1934). This may be due to the model's complexity and its need for more data to fully utilize its attention mechanism.

Visualization

Predictions vs ground truth data for PowerConsumption_Zone3, forecasting 10 hours in advance.

Test Loss: 0.0010
Mean Absolute Error (MAE): 0.1742

Model Details:

• Model: LSTM
• Hidden Size: 75
• Number of Layers: 2
• Epochs: 5
• Learning Rate: 0.001

Folder Tree

Power_Consumption_Prediction
├── data
│   ├── data_functions.py     # Contains functions for data preprocessing, loading, and transformation
│   └── powerconsumption.csv  # The dataset file
├── models
│   ├── GRU.py                # GRU model implementation
│   ├── LSTM.py               # LSTM model implementation
│   └── Transformer.py        # Transformer model implementation
├── pretrained_models         # Directory for saving and loading pre-trained models
├── train
│   ├── evaluation.py         # Script to evaluate model performance
│   └── train.py              # Script to train and save models
├── utils
│   └── argparser.py          # Contains argument parsing logic for CLI inputs
└── main.py                   # Main script to run the project

References

fedesoriano. (August 2022). Electric Power Consumption. Retrieved [Date Retrieved] from https://www.kaggle.com/datasets/fedesoriano/electric-power-consumption.