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Smart Home Energy Management

Table of Contents

1. Introduction
2. Instructions
3. Data Requirements
4. Installation
5. Usage
6. Examples
7. License

Introduction

This project presents a Smart Home Energy Management System designed to optimize power consumption thus reduce energy costs by integrating renewable energy sources and load shifting. Key features of the system include:

• Battery Management
  The system integrates a battery with a capacity equal to half of the home’s maximum peak load. For instance, if the peak load is 6 kW, the battery capacity will be 3 kWh. The charging and discharging rates are optimized at 20% (0.6 kW for a 3 kWh battery) and 30% (0.9 kW for a 3 kWh battery), respectively.

  • The battery supplies power during peak hours (17:00 - 22:00).
  • It will recharge when the State of Charge (SoC) drops below 50% and is capped at a maximum SoC of 80%.
  • Battery usage is halted once the SoC reaches 30%, ensuring the system's longevity.

• Solar Panel Integration
  By incorporating photovoltaic (PV) panels, the system harnesses renewable energy, reducing reliance on grid power and lowering the environmental impact.

• Load Shifting
  To optimize energy usage, the system implements load shifting. When the home's energy consumption exceeds a set threshold, non-essential loads are shifted outside peak hours (17:00 - 22:00) based on their priority. This helps to:

  • Lower peak demand and reduce overall energy costs.
  • Ensure essential devices continue to run during peak times, while non-critical devices are deferred.

Instructions

1. Run the program.
2. Select a load profile and a meteorological data set.
3. Set the threshold value.
4. Press "Analyze".

The program will calculate:

• Cost savings with and without load shifting.
• Cost savings from PV panels

Data Requirements

• Electric Load Profile
• Meteorological Data

Installation

1. Clone the repository:

   git clone https://github.com/EmirKahraman/Smart_Home.git

2. Navigate to the project directory:

   cd Smart_Home

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

Execute the main program:

python main.py

Examples

These values were calculated based on the load_profile_v3.xlsx and meteorological_data.csv

Solar Irradiation

The solar irradiation data was extracted from the meteorological_data.csv file, and the average irradiation was calculated for the summer and winter months.

Figure 1: Daily solar irradiation profile

Battery Status

The battery charges when solar irradiation is available and discharges during peak demand hours.

Figure 2: Battery state of charge over time

Thresholds

Thresholds represent specific values set to analyze system behavior and outputs. The program calculates results and cost savings for the following threshold levels:

Threshold 3

Figure 3: System behavior for threshold level 3

Figure 4: Cost savings for threshold level 3

Threshold 4

Figure 5: System behavior for threshold level 4

Figure 6: Cost savings for threshold level 4

Threshold 5

Figure 7: System behavior for threshold level 5

Figure 8: Cost savings for threshold level 5

Threshold 6

Figure 9: System behavior for threshold level 6

Figure 10: Cost savings for threshold level 6

License

This project is licensed under the MIT License.

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