⚡ EnerVision: Energy Consumption and Demand Forecasting

This project simulates an energy company's data-driven approach to forecasting electricity consumption using time series models like ARIMA.
It also includes trend and seasonality analysis, plus energy-saving recommendations.

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🧩 Project Overview

Scenario:
EnerVision, a smart-energy provider, wants to:

• Understand daily and weekly consumption patterns
• Detect peak hours and holidays
• Forecast next week’s energy demand
• Propose energy optimization strategies

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🧠 Methods Used

• Exploratory Data Analysis (EDA)
• Time Series Decomposition
• Forecasting: ARIMA.
• Feature Engineering (lags, rolling means)
• Visualization with matplotlib & seaborn

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📦 Dataset

• File: energy_consumption_2000_rows.csv
• Records: 2,000 hourly readings
• Features:
  • timestamp
  • consumption_kwh
  • temperature_C
  • day_of_week, hour
  • is_holiday, is_weekend, is_peak_hour, is_offpeak

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🧰 Tech Stack

Category	Tools
Data Manipulation	pandas, numpy
Visualization	matplotlib, seaborn
Time Series	statsmodels

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🚀 How to Run on Google Colab

1. Upload the following files to Colab:
   • EnerVision_Energy_Forecasting.ipynb
   • energy_consumption_2000_rows.csv