AI Net Metering Optimization
Machine learning for maximizing value under net metering tariffs
The Problem
“Optimize Net Metering Value Amid Volatile Tariffs”
Organizations face these key challenges:
Tariff complexity (TOU windows, demand charges, export caps, NEM successor rules) makes it difficult to predict customer economics and program cost under changing regulations
Inaccurate interval forecasting of load and PV exports leads to mis-sized systems, suboptimal battery/EV schedules, and unexpected bills and true-up surprises
Limited visibility into feeder-level constraints and localized congestion causes misalignment between customer incentives and grid needs, increasing operational risk and interconnection friction
Impact When Solved
The Shift
Human Does
- •Review current tariffs, net metering rules, and export compensation changes
- •Estimate customer load, PV production, and bill impacts using spreadsheets and standard profiles
- •Run manual scenario comparisons for rate plans, TOU periods, and storage sizing
- •Investigate billing disputes and annual true-up variances from interval and billing records
Automation
- •No AI-driven forecasting or optimization is used
- •Static formulas calculate estimated imports, exports, and bill credits
- •Basic reports summarize monthly usage, production, and true-up outcomes
Human Does
- •Approve tariff assumptions, program rules, and optimization objectives
- •Decide customer guidance, rate-plan recommendations, and DER operating policies
- •Review flagged anomalies, billing exceptions, and cases with feeder or interconnection constraints
AI Handles
- •Forecast interval load, PV generation, imports, exports, and bill impacts by customer and location
- •Optimize battery dispatch, EV charging alignment, and rate-plan selection under tariff and grid constraints
- •Simulate tariff, export compensation, and customer behavior scenarios across the portfolio
- •Monitor interval data for billing anomalies, forecast drift, and unexpected peak or export patterns
Operating Intelligence
How AI Net Metering Optimization 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 change tariff assumptions, program rules, or optimization objectives without approval from the responsible program or regulatory owner [S1] [S2].
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
Technologies
Technologies commonly used in AI Net Metering Optimization implementations:
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