Quick Answer: AI Decision Profitability Audit
To audit AI energy trading decisions, use a comprehensive approach involving:
- Detailed transaction logs
- Comparative performance metrics
- Real-time financial tracking
- Cross-referencing market benchmarks
Understanding AI Decision Profitability in Energy Trading
Modern Battery Energy Storage Systems (BESS) leverage sophisticated AI algorithms to optimize electricity trading, but verifying their financial performance requires a strategic, multi-layered approach.
Performance Tracking Methodology
Key Performance Indicators (KPIs)
| KPI | Description | Target Range |
|---|---|---|
| Net Trading Profit | Total revenue minus operational costs | €0.02-€0.15/kWh |
| Market Arbitrage Efficiency | Price difference exploitation | >15% annual return |
| Trading Frequency | Number of optimal buy/sell decisions | 3-7 transactions/day |
Technical Audit Process
- Download comprehensive transaction logs
- Cross-reference with EPEX SPOT market data
- Calculate individual trade profitability
- Analyze decision-making algorithms
Practical Implementation Example
Consider a 500 kWh BESS in Germany with following monthly performance:
- Total Trades: 120
- Average Profit per Trade: €37.50
- Monthly Trading Revenue: €4,500
- Operational Costs: €1,200
- Net Monthly Profit: €3,300
Frequently Asked Questions
How often should I audit AI trading decisions?
Recommended: Monthly comprehensive review, with weekly quick checks.
What tools are needed for auditing?
Specialized energy trading analytics software, market data subscriptions.