This project focuses on predicting electricity consumption using advanced time series analysis, predictive modeling, and Deep LSTM (Long Short-Term Memory) networks. The goal is to develop an accurate model for forecasting electricity demand.
- Conducted extensive and insightful Exploratory Data Analysis (EDA) on the electricity consumption dataset.
- Developed a deep LSTM model for predicting electricity consumption with improved accuracy.
- Achieved significant reductions in key metrics, including RMSE (Root Mean Square Error) to 2675.17 MW and MAPE (Mean Absolute Percentage Error) to 7.29.
- Demonstrated expertise in predictive modeling and data analysis.
- Showcased the model's precision in forecasting electricity demand.
In the initial phase of the project, I conducted a comprehensive Exploratory Data Analysis (EDA) to gain insights into the electricity consumption dataset. This EDA process helped me understand patterns, trends, and potential factors affecting electricity consumption.
The core of this project lies in the development of a Deep LSTM model for electricity consumption prediction. The model leverages the power of deep learning to provide accurate forecasts. Key steps in the model development process include:
Data Preprocessing: Cleaning, normalization, and feature engineering to prepare the data. Model Architecture: Designing a Deep LSTM network with suitable layers and parameters. Training: Training the model on historical data, fine-tuning hyperparameters, and ensuring convergence. Evaluation: Assessing the model's performance using evaluation metrics like RMSE and MAPE. Model Validation: Rigorous model validation to ensure generalization and reliability.
The Deep LSTM model has demonstrated impressive predictive power:
RMSE: 2675.17 MW (Root Mean Square Error) MAPE: 7.29 (Mean Absolute Percentage Error)
These results underscore the model's high precision in forecasting electricity demand.