Description:

The Energy Consumption Forecasting Tool is a web-based data analytics system designed to predict future electricity usage for households, commercial buildings, or industries. It uses historical energy usage data, weather information, seasonal patterns, and even special event data to generate accurate forecasts.

By applying time-series forecasting models such as ARIMA, Prophet, or LSTM neural networks, the system can help energy providers plan electricity generation, prevent blackouts, and optimize grid load distribution. Businesses and households can also use it to monitor and reduce power costs.

Key Features:

1. Historical Data Import – Upload energy consumption data from CSV, Excel, or smart meter APIs.

2. Weather & Seasonal Data Integration – Incorporates temperature, humidity, and seasonal patterns to improve accuracy.

3. Time-Series Forecasting Models – Supports ARIMA, Prophet, and LSTM models for short-term and long-term prediction.

4. Interactive Visualization – Graphs showing past consumption trends and future predictions.

5. Custom Forecast Periods – Predict energy usage for the next day, week, or month.

6. Anomaly Detection – Detects unusual spikes or drops in consumption for troubleshooting.

7. Energy Cost Estimation – Calculates estimated electricity bills based on predicted usage.

8. Mobile-Friendly Dashboard – Works seamlessly on both desktop and mobile devices.

Technology Stack:

• Backend: PHP / Java / Node.js (REST APIs for data handling and model integration)

• Frontend: HTML, CSS, Bootstrap, JavaScript (Chart.js or D3.js for visualization)

• Database: MySQL / MongoDB (for storing consumption history)

• Data Science Layer: Python (pandas, NumPy, scikit-learn, Prophet, TensorFlow for forecasting models)

• Data Sources: Smart meter APIs, weather data APIs (like OpenWeatherMap)

Example Use Case:

• A shopping mall integrates its smart electricity meters with the tool.

• The system analyzes two years of past consumption and weather records.

• It predicts a 15% increase in electricity usage for the coming summer due to high visitor footfall and air conditioning needs.

• The mall management schedules energy-saving measures and negotiates better tariffs with the energy provider.