Automation Workflow for Unifying Data from Disparate SCADA, CMS, and Weather APIs

This workflow automates the daily, multi-hour process of logging into separate SCADA portals, downloading CMS reports, and pulling weather API feeds. Agents handle authentication, pagination, and format conversion, merging time-series data into a unified schema. This eliminates the 65-80% of data engineer time typically spent on manual extraction and reconciliation, freeing them for higher-value modeling and analysis.

The build uses a LangGraph or Temporal-based orchestrator to manage specialized agents:

• Ingestion Agents: Poll OPC-UA/Modbus (SCADA), REST APIs (CMS, weather), and FTP sites.
• Validation Agents: Apply rules to flag missing intervals, implausible values (e.g., negative irradiance), and API failures.
• Harmonization Agent: Aligns timestamps to a common UTC standard, interpolates missing data, and maps disparate tag names (e.g., WTG01_Pwr to turbine_1_power_kw). Data flows through a quality checkpoint before being committed to a time-series database (e.g., TimescaleDB) as the single source of truth.

Poor data quality directly reduces forecast model accuracy, leading to suboptimal bidding and storage dispatch. By ensuring complete, clean, and timely data, this foundational workflow improves day-ahead forecast RMSE by 15-25%. This accuracy gain translates to more confident bids, reduced imbalance charges, and increased capture prices. For a 200 MW portfolio, a 2% improvement in capture price can mean >$500k annual revenue upside.

Implementation follows a pilot-first approach:

1. Phase 1: Deploy agents for the highest-value data source (e.g., plant SCADA) with detailed logging and human-in-the-loop review of merged outputs.
2. Phase 2: Add CMS (e.g., PowerFactors, AlsoEnergy) and weather API agents, implementing automatic retry and fallback to cached data on failure.
3. Phase 3: Integrate the unified data pipeline with downstream forecasting and trading systems via a published schema and API. Critical controls include dead-letter queues for failed records, data lineage tracking, and configurable alerting for sustained data gaps.

The workflow is built for auditability and control in regulated environments. Each data point is tagged with its source, ingestion timestamp, and validation status. A dashboard shows pipeline health, data freshness, and error rates. Approval gates can be configured for schema changes. This observability layer is critical for operators who must trust the data before automating million-dollar trading or dispatch decisions, and provides defensible evidence for performance reporting to asset owners and financiers.

This agent manages the secure, fault-tolerant polling of disparate external APIs. It handles authentication renewal, request throttling, and graceful degradation when a source is unavailable. For SCADA systems, it may use OPC UA or MQTT bridges; for CMS (like SAP PM or Maximo), it uses REST/SOAP APIs; for weather data, it connects to services like NOAA or commercial providers. It normalizes timestamps and initial payload structures before queuing raw data for validation.

Operating on the raw data stream, this agent applies schema validation, range checks (e.g., negative irradiance, turbine RPM beyond specs), and statistical anomaly detection. It flags missing, stale, or physically impossible values. It correlates signals across sources—for instance, checking if reported wind speed aligns with turbine power output—to identify sensor drift or calibration issues. Flagged records are routed to an exception queue for human review or automated imputation.

This agent transforms validated raw data into a unified time-series format. It performs critical spatial and temporal alignment: interpolating weather data to each asset's coordinates, resampling SCADA data to a consistent interval (e.g., 5-min), and merging CMS work-order status. It creates derived features essential for forecasting, such as equipment efficiency ratios, degradation indicators, and site-specific weather corrections. The output is a clean, queryable dataset served to a data lake or time-series database.

This agent manages the workflow for data that fails validation or requires expert judgment. It classifies exception severity, routes high-priority alerts (e.g., complete SCADA feed loss) via Slack/Teams, and creates tickets in Jira or ServiceNow. For lower-priority issues, it can suggest imputation strategies based on historical patterns. It maintains an audit trail of all actions and approvals, ensuring data governance and providing a clear lineage from raw source to cleansed output.

This agent provides continuous visibility into the entire data unification pipeline. It tracks ingestion latency, data freshness, validation error rates, and compute resource usage. It generates dashboards (e.g., in Grafana) and sends health status to centralized monitoring tools. By establishing baselines for normal operation, it can proactively alert on pipeline degradation before it impacts downstream forecasting models, ensuring the operational reliability required for automated trading and dispatch decisions.

The central nervous system built on a framework like LangGraph that defines the workflow's control flow, manages agent state, and handles conditional branching. It sequences the agents: triggering ingestion on a schedule, passing state from validation to harmonization, and invoking the exception agent when needed. It persists checkpoints for idempotency, ensuring pipeline resilience during failures. This orchestration layer is where business logic—like pausing harmonization during a CMS outage—is codified and executed.