Data Science for Climate Change: Mitigate Greenhouse Gases Emissions by Reducing Energy Consumption of Buildings (Part 1)

Challenge Background

Goal of the Project

The Project Goals:

• Data merging: The utilization of multiple datasets requires data merging. Therefore, the building dataset and other acquired datasets such as the meteorological dataset.
• Data cleaning: The process of data cleaning applied involves the removal of outliers and treatment of missing data.
• Data conversion: The data may be comprised of some categorical data in variables, which will be converted to values suited for ML algorithms.
• Data Normalization: Scaling the data to a unit norm to avoid problems during modelling. Due to the different types of data (e.g., continuous, discrete, and categorical) present in the dataset, it is essential to normalize the data to eliminate the influence of the dimension and avoid difficulties during the model development phase.
• Feature Selection: The degree to which a particular feature may impact a model will vary. We need to consider features which have a larger influence on the model and eliminate the ones that are not relevant. This also helps with Model performance. Techniques such as Filter Based: Pearson’s Correlation or Embedded Methods: Random Forest Classifier can be explored for selecting the most suitable input feature.
• Model Development: Several Models ANN, SVM, etc,...
• Model Evaluation: performance measures: R-Squared (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE). The most often utilized for energy consumption prediction are the MSE and RMSE. For the WIDS data-thon, the evaluation metric for this competition is Root Mean Squared Error (RMSE). The RMSE is commonly used measure of the differences between predicted values provided by a model and the actual observed values.

Project Timeline

What you'll learn