Home Energy Independence via Solar Power
Evaluating the Feasibility, Efficiency, and ROI of a Residential Solar Energy System
John Woodcock Jr., Christopher A. Murphy
Summer Term, 2024
Project Overview
This case study explored the energy economics and sustainability potential of a residential solar electrical system in Hawaii. The goal was to analyze the system’s ability to reduce dependence on the grid, calculate its ROI and determine how EV charging impacts overall performance.
Problem Statement
Hawaii has the highest energy and fuel costs in the United States. This study sought to determine whether a solar-equipped home with an EV could achieve energy independence and financial savings.
Methods
Normalized timestamps, calculated kWh, and aggregated to a daily series.
Compared solar capture vs. load (with/without EV), assess battery surplus/deficit and overnight availability.
Evaluated model grid vs. solar costs, EV vs. gasoline equivalents, and ROI.
Key Learnings
To remain off-grid overnight, the battery must retain at least 81% charge when solar production ends.
Batteries were full by ~4PM and depleted by ~8AM.
EV charging made this threshold hard to achieve; grid draw was required on most EV charge days.
EV solar charging saved an additional $69.87, reinforcing the value of switching from gasoline to solar-based transportation.
EVs significantly strain energy systems; timing, frequency, and duration of charging must be carefully optimized.
Even without full independence, net savings from solar + EV use are substantial and grow over time.
ROI is driven by cost avoidance, which is amplified in regions with high energy prices (like Hawaii).
Visualizations
Competencies Employed
Exploratory Data Analysis (EDA)
Discovering patterns and relationships in data using visual and statistical techniques.
Python Coding
Developing data pipelines, machine learning models, and automation tools using Python’s data science ecosystem (e.g., pandas, scikit-learn, TensorFlow)
Strategy & Decisions
Developing alternative strategies based on the data analysis.
Domain-Specific Analytics
Applying analytics to specific sectors such as marketing, finance, or operations.
Insights & Recommendations
Turn analysis into clear, prioritized stakeholder actions with rationale, trade-offs, and measurable outcomes.
Data Collection
Acquire, ingest, validate, and organize data using reproducible workflows and transformations to ensure compatibility with downstream data-science algorithms.
Time Series Analysis
Modeling temporal data for forecasting and trend analysis.
Descriptive Analytics
Summarizing and interpreting historical data to identify patterns, trends, and insights that inform decision-making and business understanding.
Data Visualization
Presenting data insights clearly using charts, dashboards, and visual storytelling.
Visual Analytics
Designing interactive tools that allow users to explore and analyze data visually.
Additional Technical Information
Data Source(s)
Data included solar power generated by the Tesla system, household and EV load drawn from the system, electricity rates from the Hawaiian Electric Company (HECO) and local automobile gas prices.
Results Summary
The system is generally sufficient without EV charging, but falls short when EV charging is included.
Without EV charging, the home was often fully solar-powered; with EV charging, the grid was frequently needed.
Recommended Actions
Increase battery storage to support overnight energy needs with EV charging.
Shift EV charging to early afternoon, when solar production peaks.
Stagger or shorten charging sessions to better align with solar capacity.
Expand PV panel coverage to boost total daily generation.
Integrate smart home energy management systems to automate and optimize energy distribution.