Smart Energy Consumption
Smart Energy Consumption
AI Advanced Level
- Submission
- Requirements
- Target
- Dataset
- Overview
Submission must include🔹
Trained RL agent✔️
Custom environment code✔️
Results and comparisons✔️
Brief report (methodology + insights)✔️
Key Takeaways🚀
Dataset: Household energy consumption over 4 years📌
Goal: Train an RL agent to reduce energy consumption while maintaining comfort📌
Method: Reinforcement learning using DQN, PPO, or A2C📌
Evaluation: Energy savings, model performance, and agent learning efficiency📌
Model Requirements🛠️
Reinforcement Learning Agent✅
: Participants must use reinforcement learning techniques such as
Deep Q-Networks (DQN)
Proximal Policy Optimization (PPO)
Advantage Actor-Critic (A2C)
The RL agent must learn to predict and control energy consumption in a smart home environment.
Custom Environment Creation✅
Participants must simulate a smart home environment using
OpenAI Gym
Custom Python-based simulation
The environment should have
State Variables: Global_active_power, Time, Voltage, etc
Actions: Adjust heating, cooling, or turn devices on/off
Rewards: Negative rewards for high energy usage; positive rewards for efficiency
Target of the Model🎯
:The goal of this challenge is to train a reinforcement learning agent to
Predict and optimize energy consumption in real-time✅
Learn when to turn off/on appliances based on consumption patterns✅
Reduce unnecessary power usage without affecting household comfort✅
Adapt to time of day, peak hours, and external conditions (e.g., voltage fluctuations).The agent will interact with a simulated environment (created by participants) and receive rewards based on energy-saving performance
Dataset Description
This dataset contains measurements of electric power consumption in a single household recorded at one-minute intervals over nearly four years (from December 2006 to November 2009)
Total Records: 2,000,000
Time Range: December 2006 – october 2010
Frequency: 1-minute intervals
Features (Columns in the Dataset)🔹
Data Dictionary
| Column Name | Description |
| Date | Date of the record (DD/MM/YYYY) |
| Time | Time of the record (HH:MM:SS) |
| Global_active_power | Total household power consumption (kilowatts) |
| Global_reactive_power | Reactive power in the household (kilowatts) |
| Voltage | Household voltage (volts) |
| Global_intensity | Intensity of electricity usage (amperes) |
| Sub_metering_1 | Energy used by kitchen appliances (e.g., fridge, microwave) |
| Sub_metering_2 | Energy used by laundry appliances (e.g., washing machine, dishwasher) |
| Sub_metering_3 | Energy used by heating/cooling devices (e.g., air conditioning, electric water heater) |
Note
The dataset contains missing values and needs preprocessing before training models
Global_active_power is the primary target for energy optimization
Dataset downloaded
https://drive.google.com/file/d/1GR-M98O6PHBehGGNKns__KUUO0RtCJvl/view?usp=drive_link
Smart Energy Consumption 📌
Problem Statement
With rising energy costs and increasing environmental concerns, optimizing home electricity usage is a key challenge. The goal of this competition is to develop an AI reinforcement learning agent that learns to optimize energy consumption in a smart home while maintaining user comfort.Participants will use real-world smart home energy data to train an AI agent that can predict energy consumption patterns and make energy-saving decisions dynamically
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