To develop a machine learning model that can accurately predict energy consumption in educational institutions based on historical data.
To analyze the factors influencing energy consumption in educational settings and identify key variables for prediction.
To evaluate the performance of the machine learning model in predicting energy consumption and compare it with traditional forecasting methods.
Collect and preprocess historical energy consumption data from educational institutions.
Identify and extract relevant features that may impact energy consumption in educational settings.
Develop and train a machine learning model using algorithms such as regression, clustering, or time series analysis.
Evaluate the performance of the model using metrics like RMSE, MAE, and R-squared.
Compare the results with traditional forecasting methods and analyze the effectiveness of the machine learning approach in predicting energy consumption.
