Accurate energy consumption forecasting is crucial for optimizing power distribution, improving energy efficiency, and supporting sustainable development. This project explores time series forecasting techniques to predict energy usage based on historical consumption data. Utilizing machine learning models such as Facebook Prophet and XGBoost, the project aims to compare the performance of statistical and ensemble-based approaches in energy demand forecasting. The dataset, sourced from the UCI Energy Repository, is preprocessed and analyzed to extract meaningful trends, seasonal patterns, and anomalies. Model evaluation is conducted using key metrics such as MAE and RMSE, while interactive visualizations provide insights into energy consumption trends and future predictions. The results highlight the advantages of different forecasting methodologies and their applicability to real-world energy management scenarios.
Methodology
This project employs a structured approach to forecast energy consumption using time series analysis. The methodology consists of several key stages: data acquisition, preprocessing, exploratory data analysis (EDA), model selection and training, performance evaluation, and visualization.
The dataset used in this study is sourced from the UCI Energy Repository, containing historical household energy consumption records. The data includes time-stamped energy usage values, which serve as the foundation for time series forecasting.
Before modeling, the raw dataset undergoes preprocessing steps to ensure data quality and consistency:
Handling missing values: Missing entries are imputed using interpolation techniques.
Timestamp formatting: The dataset is converted into a time-series format with a consistent frequency (e.g., hourly or daily aggregation).
Feature engineering: Additional features, such as lagged consumption values and time-based indicators (e.g., hour of the day, day of the week, season), are created to improve model performance.
Outlier detection: Anomalous values are identified and handled using statistical methods like the Interquartile Range (IQR) or Z-score analysis.
EDA is conducted to uncover patterns and trends in energy consumption, including:
Trend analysis: Identifying long-term variations in energy usage.
Seasonality detection: Recognizing recurring consumption patterns (daily, weekly, seasonal).
Correlation analysis: Understanding relationships between energy usage and external factors (e.g., time of day).
Decomposition: Using statistical methods (e.g., seasonal decomposition of time series) to break the data into trend, seasonal, and residual components.
Two primary forecasting approaches are implemented:
Facebook Prophet: A statistical model designed for time series forecasting, capable of handling trend and seasonality automatically. Prophet is used to capture long-term patterns and periodic fluctuations in energy consumption.
XGBoost (Extreme Gradient Boosting): A powerful machine learning algorithm that learns complex patterns by leveraging past consumption data as features. Lagged values and engineered time-based features are incorporated to enhance predictive accuracy.
Both models are trained using historical data, with hyperparameter tuning applied to optimize performance. The dataset is split into training and test sets to evaluate generalization capabilities.
To assess forecasting accuracy, the models are evaluated using standard error metrics:
Mean Absolute Error (MAE): Measures the average absolute difference between predicted and actual values.
Root Mean Square Error (RMSE): Quantifies the standard deviation of prediction errors, giving higher weight to larger errors.
Mean Absolute Percentage Error (MAPE): Expresses the prediction error as a percentage of actual values.
A comparative analysis of Prophet and XGBoost is performed to determine which model provides more accurate and reliable forecasts.
The final step involves generating interactive visualizations to present the results effectively:
Time series plots: Displaying actual vs. predicted energy consumption.
Trend and seasonality charts: Highlighting recurring patterns in the data.
Error distribution graphs: Visualizing model performance across different time periods.
By following this structured methodology, the project aims to develop a robust energy forecasting system that can assist in optimizing energy management and planning.
MAE: 0.5815
MSE: 0.6653
RMSE: 0.8157
MAPE: 99.11%
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