Household Energy Consumption Simulator

A Python-based simulation tool for modeling and comparing household electricity consumption patterns at 15-minute resolution.

Project Overview

This project simulates household energy consumption over multiple days, comparing different scenarios:

• Baseline Household: Standard appliances and typical usage patterns
• Energy-Saving Household: Efficient appliances and reduced consumption
• PV Household: Solar panel integration with net consumption calculation

Features

✓ High-resolution simulation (15-minute intervals, 96 steps per day)
✓ Realistic consumption model combining base load, daily patterns, and appliance events
✓ Stochastic variation for day-to-day differences
✓ Multiple appliances (washing machine, dishwasher, cooking)
✓ Cost and CO₂ estimation
✓ Comprehensive visualizations
✓ Scenario comparison with detailed analytics

Technical Requirements

• Python 3.8+
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Jupyter Notebook

Installation

1. Clone or download this repository
2. Install dependencies:

pip install -r requirements.txt

Usage

1. Start Jupyter Notebook:

jupyter notebook

2. Open household_energy_simulator.ipynb

3. Run all cells sequentially (Cell → Run All)

Project Structure

HouseholdEnergyConsumptionSimulator/
├── household_energy_simulator.ipynb  # Main notebook with all code and analysis
├── requirements.txt                   # Python dependencies
└── README.md                          # This file

Notebook Contents

1. Introduction & Model Overview

• Explanation of simulation approach
• Model components and assumptions

2. Configuration Parameters

• Adjustable simulation settings
• Electricity pricing and CO₂ factors

3. Core Simulation Functions

• generate_time_index(): Create time series
• base_load(): Calculate always-on consumption
• daily_profile(): Time-of-day patterns
• simulate_period(): Main simulation engine

4. Appliance Models

• Washing machine (high power, occasional use)
• Dishwasher (medium power, frequent use)
• Cooking (variable power, multiple daily events)

5. Scenario Definitions

• Baseline parameters
• Energy-saving parameters
• PV system configuration

6. Analytics & Visualization

• Daily totals calculation
• Cost and CO₂ estimation
• Comparison plots

7. Results & Interpretation

• Summary statistics
• Scenario comparison table
• Multiple visualization types
• Detailed interpretation

8. Conclusions

• Key findings
• Model limitations
• Possible extensions

Customization

All parameters are easily adjustable at the top of relevant sections:

Simulation Settings:

• SIMULATION_DAYS: Number of days to simulate (default: 14)
• STEP_MINUTES: Time resolution (default: 15)
• ELECTRICITY_PRICE: Cost per kWh (default: 0.30 €)
• CO2_PER_KWH: CO₂ intensity (default: 0.420 kg)

Scenario Parameters:

• Base load consumption
• Peak multipliers (morning/evening)
• Appliance power ratings
• Appliance usage probabilities
• PV system capacity

Example Results

Typical findings from a 14-day simulation:

Scenario	Total kWh	Cost (€)	CO₂ (kg)	Savings vs Baseline
Baseline	~280 kWh	~84 €	~118 kg	-
Energy-Saving	~180 kWh	~54 €	~76 kg	~30 € (36%)
PV Household	~120 kWh	~36 €	~50 kg	~48 € (57%)

Note: Actual values vary due to stochastic elements

Key Insights

1. Energy-saving measures (efficient appliances, reduced base load) can reduce consumption by 30-40%
2. Peak consumption occurs during evening hours (17:00-22:00) due to combined appliance usage
3. PV systems can offset 50-60% of electricity costs by reducing daytime grid consumption
4. High-power appliances (washing machine, cooking) create significant consumption spikes

Presentation Tips

For your seminar talk (~30 minutes):

1. Introduction (5 min): Explain the problem and model approach
2. Code walkthrough (10 min): Show key functions and explain logic
3. Results (10 min): Present visualizations and comparison table
4. Discussion (5 min): Interpret findings and answer questions

Key points to emphasize:

• How randomness creates realistic variation
• Why scenarios differ (parameter changes)
• What the results mean practically
• Model limitations and possible improvements

Model Assumptions

• Simplified appliance models (constant power during operation)
• No seasonal effects (heating/cooling)
• Fixed electricity price (no time-of-use tariffs)
• PV production follows idealized sine curve
• No battery storage

Possible Extensions

• Weekend vs weekday behavior
• Temperature-dependent heating/cooling
• Time-of-use electricity tariffs
• Battery storage for PV scenario
• Electric vehicle charging patterns
• Seasonal variation in consumption and PV production

License

This project is created for educational purposes (Portfolio Project 3, THWS).

Author

Created by Alexander Lemeshov
January 2026