⚡📊 Energy Consumption Forecasting using Weather & Market Data (Time-Series ML Project)

🔥 End-to-End Data Science Pipeline | Time-Series Forecasting | Real-World Energy Dataset

Excited to share a deep dive into my recent project focused on predicting electricity consumption using comprehensive market and weather data. From raw data to a predictive model—here’s the pipeline:

1. Tech Stack : Leveraged Python with core libraries like Pandas for data manipulation, NumPy for numerical operations, and the Scikit-learn ecosystem for machine learning and modeling. Matplotlib and Seaborn were used for high-impact visual analytics.

2. Data Acquisition : Successfully acquired and loaded the extended_data_v2.csv dataset, encompassing 6,456 hourly observations of energy market, consumption, and detailed weather variables. The process prepared the foundation for a time-series analysis project.

3. Initial Data Exploration and Basic Filtering : A critical first step confirmed the dataset was complete with no missing values and involved identifying data types, noting the need to convert the datetime column, and analyzing initial variable distributions. Established electricity_consumption as the primary target variable.

4. Univariate, Bivariate, and Multivariate Analysis and Visualisation : Executed extensive EDA, revealing key insights: Consumption has a strong $0.64$ positive correlation with air_temperature and is notably lower on public holidays. Visualizations confirmed these relationships across various weather conditions.

5. Feature Engineering : Enriched the model with new features by converting the datetime column and extracting granular time-based features (Year, Month, Hour, Day of Week). This was essential for capturing seasonal and hourly consumption patterns.

6. Define Target and Features; Split Data : Defined the final feature set (18 variables) and the target variable, electricity_consumption. The dataset was then robustly split into $80%$ training and $20%$ testing sets for unbiased model development.

7. Train and Evaluate Regression Models : Implemented and trained various Regression Models (e.g., Linear Regression) on the training data. The models were evaluated using appropriate metrics (like MSE or R-squared) to assess their predictive accuracy against actual consumption.

8. Visualise Regression Model Performance : Used charts to visually compare the model's predictions against the actual electricity consumption values in the test set. This confirmed model performance and highlighted areas of highest and lowest predictive fidelity.

9. Summary : The project delivered a well-performing predictive model based on comprehensive EDA. Key findings included the significant impact of temperature and public holidays on consumption, successfully transforming raw data into actionable intelligence.