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Electric consumption prediction for time-series data using ML and DL models.

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MLTS_Project

MLTS_Project is a data-driven project aimed at predicting electric consumption for charging stations using an open-source dataset from Boulder, Colorado's open data portal. The dataset contains detailed records of electric vehicle charging transactions at various city-owned charging stations in Boulder. This project explores and compares several machine learning and deep learning techniques, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional Long Short-Term Memory (BiLSTM), Linear Regression, Random Forest, and Support Vector Regression (SVR) for the prediction of power consumption based on one year of real electric vehicle load data.

Project Structure

The repository is organized as follows:

  • main.py: This file loads the dataset and configures model parameters.
  • utils/utils.py: Contains utility functions for handling outliers, splitting the dataset into training and testing sets, and calculating evaluation metrics.
  • models/: This directory contains the implementations of the three deep learning models used in the project.
  • optimization/optimization.py: Used for training and testing the models.

Feel free to explore the code and experiment with different models for electric consumption prediction.

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Electric consumption prediction for time-series data using ML and DL models.

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