AI · Energy Analytics · Academic project

AI-Based Load Forecasting

Short-term electrical load prediction from historical and weather data.

PythonScikit-learnPandasNumPyMatplotlib

Models Evaluated

~2.55% MAPE

Best Accuracy

CNN-LSTM Hybrid

Best Model

Source

Overview

Accurate short-term electrical load forecasting is essential for maintaining grid stability, optimizing power generation, and improving energy efficiency. This project develops an end-to-end forecasting pipeline using historical load patterns, weather information, and engineered temporal features to predict future electricity demand.

Multiple machine learning and deep learning models were developed and evaluated, demonstrating how feature engineering, time-series preprocessing, and model selection together influence forecasting accuracy for real-world energy systems.

Problem

Power utilities require accurate short-term load forecasts to balance electricity generation, minimize operational costs, and maintain grid reliability. This project develops a data-driven forecasting pipeline using historical demand and weather data to evaluate multiple machine learning and deep learning approaches for reliable short-term load prediction.

Process

Data pipeline

Historical load merged with weather-station features (temperature, humidity, wind) on a shared hourly timestamp index, with interpolation handling for missing intervals.

Feature engineering

Lag features, rolling means, and calendar features (hour-of-day, day-of-week, holiday flags) engineered to capture demand seasonality — this step moved the accuracy needle more than model choice did.

Model development

Developed and evaluated multiple forecasting models including Linear Regression, Decision Tree, Random Forest, Support Vector Regression, LSTM, GRU, CNN, and a CNN-LSTM hybrid architecture to compare traditional machine learning with deep learning approaches.

Walk-forward validation

Time-ordered, walk-forward validation instead of a random train/test split — critical for time-series data, since a random split silently leaks future information into training and produces misleadingly good numbers.

Results

Developed and benchmarked eight forecasting models spanning both traditional machine learning and deep learning approaches for short-term electrical load prediction.

The CNN-LSTM hybrid architecture achieved the highest forecasting accuracy, delivering approximately 2.55% Mean Absolute Percentage Error (MAPE) on the evaluation dataset.

Feature engineering—including lag features, rolling statistics, and temporal attributes—significantly improved forecasting performance across all models.

Demonstrated that combining convolutional feature extraction with temporal sequence learning provides more accurate forecasts than traditional machine learning models for complex electricity demand patterns.

Learnings

Feature engineering played a critical role in forecasting performance, often contributing as much as model selection itself.

Time-series validation strategies such as walk-forward evaluation are essential for obtaining realistic forecasting performance and avoiding data leakage.

Deep learning models, particularly hybrid CNN-LSTM architectures, proved more effective at capturing both local patterns and long-term temporal dependencies in electrical load data.

Building an end-to-end forecasting pipeline reinforced the importance of data quality, preprocessing, and reproducible experimentation in machine learning projects.