This project involves analyzing solar electricity generation, electricity usage, and battery usage data to assess the potential benefits of incorporating a battery system into the energy setup. Using Python and its powerful libraries, the analysis evaluates cost savings and energy efficiency improvements that a battery system could bring.
The project investigates:
Solar electricity generation. Electricity usage patterns. Potential cost savings from installing a battery to store excess solar electricity. While the analysis indicates that installing a battery saves approximately $352 per year, the high battery cost ($4,000) and system cost ($6,800) make the investment economically unviable at the current solar generation and electricity usage levels.
Data visualization: Visual comparison of solar electricity generation and electricity usage across different hours of the day. Cost analysis: Quantification of electricity costs with and without a battery system. Energy modeling: Simulation of battery charge levels to assess its contribution to energy savings. Monthly aggregation: Insights into energy generation, usage, and cost savings on a broader timescale.
- Data Visualization and Initial Checks Goal: Visualize and compare average hourly solar electricity generation and electricity usage. Libraries: pandas, matplotlib. Input: Junior Data Analyst _ Data.xlsx.
- Electricity Bought Calculation Goal: Determine electricity required from the provider. Method: Subtraction of solar generation from usage using numpy.
- Excess Solar Generation Calculation Goal: Identify surplus solar energy that can potentially charge a battery. Method: Element-wise subtraction using numpy.
- Battery Charge Level Modeling Goal: Simulate battery performance with a capacity of 12.5 kWh. Method: Iterative calculation of charge levels, capped at maximum capacity.
- Electricity Bought with Battery Goal: Calculate reduction in electricity bought due to battery usage. Method: Adjust electricity bought by factoring in battery-stored energy.
- Savings Calculation Goal: Quantify yearly cost savings from the battery system. Electricity Price Assumption: $0.17 per kWh.
- Data Aggregation and Visualization
Goal: Monthly aggregation of solar generation, electricity usage, and costs. Visualization: Bar plots to compare monthly metrics.
Cost Savings: Annual savings of $352. Conclusion: Installing a battery at the current system capacity is not economically viable.
Programming Language: Python.
pandas: Data manipulation and analysis. numpy: Numerical operations. matplotlib: Data visualization. datetime: Time-related data handling.
Clone the repository: bash Copy code git clone https://github.com/username/repo-name.git Install dependencies: bash Copy code pip install -r requirements.txt Place the Junior Data Analyst _ Data.xlsx file in the root directory.
The project includes visualizations such as:
Average hourly solar generation vs. electricity usage. Monthly comparison of energy metrics.
This project demonstrates data-driven decision-making in energy management. While the integration of a battery system offers savings and improved energy efficiency, the high initial cost currently outweighs the financial benefits.