International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| ABSTRACT | Modern energy platforms orchestrate dozens of external APIs-grid market interfaces, fleet telematics, billing systems, weather services, regulatory reporting endpoints-into complex workflows that power demand response, fleet charging, energy trading, and customer operations. As these platforms scale to manage thousands of EVs across multiple grid markets, the orchestration layer itself becomes a critical bottleneck: manual workflow configuration is slow, rule-based error handling misses cascading failures, and static API routing wastes both latency budget and cost. This paper presents an AI-powered API orchestration engine that replaces rule-based workflow management with eight specialized ML models: an intent classifier that routes incoming requests, a graph neural network workflow planner that generates optimal API call sequences, an anomaly detector that predicts API failures 15 minutes before they occur, an RL-based SLA optimizer that dynamically tunes timeouts and retries, and four additional models for cost allocation, schema mediation, failure prediction, and load balancing. Through a 16-month production deployment managing 42 external API integrations and 85 internal microservices for a fleet of 10,200 EVs across four grid ISO markets, the platform achieves a 98.7% workflow success rate (up from 72.4% with manual orchestration), reduces average workflow execution time from 42 minutes to 2.4 minutes, auto-resolves 94.2% of API errors with a mean time to recovery of 12 seconds, and reduces per-workflow cost from $18.50 to $1.15-a 93.8% reduction. The platform manages $45.6M in annual energy transaction revenue and achieves break-even ROI in 7 months. Five energy vertical case studies validate cross-domain effectiveness. |
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| TITLE |
AI-Powered API Orchestration and Intelligent Workflow Automation in Large-Scale Energy Platforms RANGA RAYA REDDY ERAGAMREDDY Lead Software Engineer, Austin, Texas, United States DOI: 10.15680/IJIRCCE.2026.1401001 |
| AUTHOR | RANGA RAYA REDDY ERAGAMREDDY Lead Software Engineer, Austin, Texas, United States |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401001 |
| pdf/1_AI-Powered API Orchestration and Intelligent Workflow Automation in Large-Scale Energy Platforms.pdf | |
| KEYWORDS | |
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