Electricity consumption is an important economic index and plays a significant role in drawing up an energy development policy for each country. To obtain an accurate forecasting of demand, a more sophisticated model is required, which can take in to account various non-linearities present in historical demand and other featured data. This project deploys a simple deep neural network to predict the hourly electricity consumption of 'Scafie building" in the Carnegie Mellon University.
Process to Run the File:
- First Run Scrap Weather Data.ipynb file to collect the weather Data. Although the file is uploaded in Data Folder.
- Then run the Data Cleaning and Processing.ipynb file, to create train, validation and test files.
- Run Linear Regression.ipynb File to Get the Linear Regression results.
- Run Simple Neural Network.ipynb File to Get NN Results.
- Run C-RNN,ipynb File to get C_RNN model Results.