AI Off-Grid Solar Sizing
The Problem
“Accurate off-grid solar sizing amid uncertain loads”
Organizations face these key challenges:
Sparse or inaccurate customer load data leads to chronic under- or over-sizing and unpredictable performance
High variability in solar resource, temperature, and seasonal demand is not captured in deterministic spreadsheet sizing
Manual sizing is slow, inconsistent across engineers/contractors, and difficult to audit for financiers and regulators
Impact When Solved
The Shift
Human Does
- •Collect customer appliance lists, site survey notes, and rough daily energy estimates
- •Estimate PV, battery, inverter, and generator sizes using rules of thumb and spreadsheet calculations
- •Apply safety factors, autonomy assumptions, and component selection based on engineer judgment
- •Review design tradeoffs for cost versus reliability and prepare the proposal or quote
Automation
- •No meaningful AI support in the legacy sizing workflow
- •No automated load profile inference from sparse customer inputs
- •No probabilistic solar or seasonal scenario analysis
- •No automated optimization of lifecycle cost against reliability targets
Human Does
- •Confirm customer requirements, critical loads, uptime targets, and site constraints
- •Review AI-generated sizing recommendations and approve the preferred design option
- •Handle exceptions where site conditions, logistics, or customer priorities differ from model assumptions
AI Handles
- •Infer realistic hourly load profiles from sparse inputs such as appliance lists, occupancy, and historical usage signals
- •Generate site-specific solar, temperature, and seasonal demand scenarios from available weather and location data
- •Optimize PV, battery, inverter, and generator sizing to meet reliability targets at lowest lifecycle cost
- •Produce auditable design options with expected CAPEX, LCOE, diesel use, and battery replacement implications
Operating Intelligence
How AI Off-Grid Solar Sizing runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not finalize a customer-facing system design without review and approval from a solar design engineer or energy project manager [S1][S3].
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Real-World Use Cases
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Use AI to decide when a house should use solar power, charge or discharge a battery, or draw electricity from other sources so the home microgrid operates more efficiently.