Electricity Load Prediction Model Development

Electricity Load Prediction System for New Delhi – Detailed Project Description

I developed a comprehensive Electricity Load Forecasting System designed to predict hourly and daily electricity consumption for New Delhi using advanced Machine Learning and Deep Learning approaches. The objective of this project was to help power distribution authorities maintain grid stability, manage peak demand more efficiently, and reduce operational costs through accurate, data-driven forecasting.

Project Goal

The primary goal was to build a reliable forecasting model that uses historical electricity usage along with environmental and seasonal variables to generate accurate load predictions. This system enables electricity boards to plan distribution, prevent overloads, and optimize energy generation.

Data Processing and Feature Engineering

The project involved working with historical electricity load data, meteorological data such as temperature and humidity, seasonal and monthly variations, time-of-day and day-of-week cycles, and indicators for holidays or special events.
All data was cleaned, normalized, and aligned into a unified time-series format.
Additional features were engineered to enhance model performance, including lagged load values for temporal dependency, moving averages for smoothing, categorical representations for days and seasons, and temperature–load correlation inputs to capture weather-driven variations.

Machine Learning and Deep Learning Models

Multiple algorithms were implemented and tested to identify the most accurate forecasting model.
A baseline Linear Regression model was used to capture simple trends and to provide a comparison benchmark.
A Random Forest Regressor was then developed to model non-linear consumption patterns and improve predictive performance.
Finally, an LSTM (Long Short-Term Memory) neural network was built as the primary model. LSTM was chosen because of its ability to learn long-term temporal patterns present in electricity usage data. The network consisted of stacked LSTM layers, dense output layers, and was trained using sliding-window time-series sequences. Techniques such as early stopping and careful hyperparameter tuning were used to ensure optimal performance. This model achieved the highest accuracy among all approaches.

Model Output and Visualization

The final system generates hourly and daily demand predictions accompanied by visual analyses.
These include trend curves, comparisons between actual and predicted loads, seasonal variation graphs, and temperature–load relationship plots.
These visual outputs provide a clear, practical understanding of electricity demand behavior and make it easier for operators to plan resource allocation.

Technology Stack

The project was developed using Python with libraries such as NumPy and Pandas for data processing, Scikit-learn for machine learning algorithms, TensorFlow/Keras for building the LSTM model, and Matplotlib/Seaborn for all visualizations. Jupyter Notebook was used as the development environment.

Outcome and Impact

The LSTM-based solution delivered high forecasting accuracy and provided actionable insights into energy consumption trends.
With better predictions, electricity planning becomes more efficient, power cuts and overload risks are reduced, and distribution becomes more cost-effective.
This project received recognition as part of a team that ranked among the Top 30 in the Smart India Hackathon.