Energy Consumption Forecasting
Energy Consumption Forecasting
AI Intermediate Level
- Submission
- Requirements
- Target
- Dataset
- Overview
Submission must include🔹
Data cleaning steps ✔️
Data preparation and preprocessing ✔️
Data visualization (graphs/charts) ✔️
ML Life Cycle application ✔️
Applying a suitable ML or DL algorithm ✔️
Brief report (methodology + results + insights) ✔️
Key Takeaways🔹
Dataset
Energy Consumption Dataset – UCI / Kaggle
Goal
Build a forecasting model to predict household energy consumption based on smart meter readings, helping optimize energy usage and support energy-saving decisions.
Evaluation
Use metrics like MAE (Mean Absolute Error), RMSE, and R² Score to assess model performance. Include visual comparisons (e.g., actual vs predicted charts).
📝 Task Requirements
To complete this task, each student is expected to apply the full machine learning workflow using the Energy Consumption Forecasting dataset. The goal is to predict and analyze patterns in household energy usage using historical time-series data.
The following components must be included in your submission
Problem Understanding
Clearly define the objective: predicting household energy consumption patterns based on historical smart home sensor data.
Data Preprocessing
Exploratory Data Analysis (EDA)
Feature Engineering
Modeling.
Evaluation
Documentation
Submit a brief technical report (PDF or Word) explaining your approach, analysis, and final conclusions
Presentation
Each student must prepare a clean and professional PowerPoint presentation (PPT) to present their complete workflow and findings, including visualizations and key metrics.
Target
The target variable in the Energy Consumption Forecasting dataset is
Global_active_power
This column represents the total active power consumed by the household at a given minute.
It’s a regression task, aiming to predict continuous numeric values (in kilowatts).
| Value Example | Meaning |
|---|---|
1.234 | The household consumed 1.234 kW at that time |
0.456 | The household consumed 0.456 kW |
2.987 | The household consumed 2.987 kW |
Note
The values in Global_active_power are continuous real numbers. Your model should be trained to predict future values based on historical patterns and features derived from the dataset.
Dataset Description
Source: Smart Home Dataset (Kaggle / UCI Smart Energy Dataset)
Records: ~2,000,000 time-series entries
Time Range: December 2006 – October 2010
Frequency: 1-minute intervals
Data Type: Time-series with real sensor readings from a single household
Target: Predict total household energy usage (Global_active_power) in kilowatt
Note
The dataset contains missing values, outliers, and noise
Dataset downloaded
Energy Consumption
Problem Statement
With the rising cost of energy and the global push for sustainability, predicting and optimizing energy consumption has become a critical challenge.
The goal of this task is to develop a predictive model that can forecast household energy consumption using real-world time-series data collected from smart meters.
Participants will apply the complete ML lifecycle to build a model capable of capturing energy usage patterns and help in making data-driven energy management decisions.
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