IoT-Enabled Smart Grid Load Forecasting Using Deep Learning

This research project develops a short-term electricity load forecasting model for a smart-grid context, using historical consumption data collected from IoT-enabled smart meters alongside weather and calendar features (temperature, day-of-week, holidays) known to influence demand patterns.

An LSTM-based sequence model is trained to forecast load at multiple horizons (1-hour, 24-hour ahead) and is benchmarked against classical time-series baselines (ARIMA, seasonal-naive) and a gradient-boosted-tree model on lagged features, using MAPE and RMSE as evaluation metrics.

The paper discusses how accurate short-term load forecasts support grid operators in demand-response scheduling and renewable-integration planning, and examines forecast degradation during anomalous periods (holidays, extreme weather) as a direction for future model robustness improvements.

Detailed Project Description

IoT-Enabled Smart Grid Load Forecasting Using Deep Learning applies machine learning to a real dataset: it prepares the data, trains and evaluates a model, and exposes the trained model through a simple interface so predictions can be made on new inputs.

How It Works
1. The dataset is collected/loaded, cleaned and explored (missing values, encoding, scaling).
2. Features are engineered and the data split into training and test sets.
3. One or more models are trained and compared using accuracy, precision/recall, F1 or RMSE.
4. The best model is saved and served through a web/GUI form that returns predictions.

Technology Roles
- Python
- TensorFlow
- LSTM
- Time Series
- Smart Grid

Implementation Steps
1. Collect the dataset (public repository or own data) and understand each attribute.
2. Set up the environment (Python, TensorFlow/Keras, Pandas) and load the data into a notebook.
3. Clean the data: handle missing values, outliers, encoding and scaling; visualise distributions.
4. Split into train/validation/test sets and engineer relevant features.
5. Train several candidate models, tune hyper-parameters and compare metrics.
6. Save the best model and wrap it in a simple web/GUI form for live predictions.
7. Document results with plots, confusion matrix / error charts and conclusions.

Key Features
- Data pre-processing pipeline
- Model comparison
- Evaluation metrics and plots
- Prediction interface

Expected Output
On completion the student will have evaluation results (metrics, confusion matrix or error plots) and a working prediction form.

Deliverables
- Notebook / source code with comments
- Cleaned dataset and trained model file
- Evaluation report with metrics and plots
- Prediction interface