Automated energy usage tracking and reporting (fuel, electricity, etc.)

Purpose

1.1. Automate the data collection of energy consumption (fuel, electricity, alternative sources) across all alternative fuel station assets for real-time oversight and accurate reporting.
1.2. Eliminate manual data entry errors, ensure compliance with environmental regulations, and enable analytics on energy usage trends for cost optimization and sustainability benchmarks.
1.3. Streamline the aggregation of data from meters, IoT devices, supplier feeds, and facility management systems into operational dashboards and automated reporting workflows.

Trigger Conditions

2.1. Scheduled intervals: hourly, daily, weekly, or on a real-time basis when sensors pass new data.
2.2. Manual request from operations dashboards or management portals.
2.3. Threshold-based: when consumption exceeds preset parameters or anomalies are detected.
2.4. Event-driven: new meter installation, service ticket resolution, system restart, or device firmware updates.

Platform Variants

3.1 Microsoft Azure IoT Hub
• Feature/Setting: Configure Event Grid trigger for device telemetry; sample - set up route for incoming meter data to trigger function execution.

3.2 Amazon Web Services (AWS) IoT Core
• Feature/Setting: Rule Actions → Send data to Lambda; sample - configure SQL-based rules for incoming messages, forwarding data to Lambda for processing.

3.3 Google Cloud IoT Core
• Feature/Setting: Device telemetry → Pub/Sub topic integration; sample - route all device messages to Google Cloud Functions via Pub/Sub.

3.4 Siemens MindSphere
• Feature/Setting: Asset Monitoring Service API; sample - subscribe to time-series energy metrics and configure webhook delivery to reporting system.

3.5 Schneider Electric EcoStruxure
• Feature/Setting: Power Monitoring Expert REST or SOAP API; sample - periodic GET requests to pull usage data for defined endpoints.

3.6 Honeywell Forge
• Feature/Setting: Data Integration Studio; sample - schedule job runs to extract, transform, and load (ETL) energy readings.

3.7 IBM Maximo
• Feature/Setting: Maximo Integration Framework (MIF) endpoint; sample - configure outbound automation script to initiate on meter updates.

3.8 EnergyCap
• Feature/Setting: API Endpoint 'Meters/GetReadings'; sample - schedule automated calls to retrieve interval consumption data.

3.9 UtilityAPI
• Feature/Setting: Automated Data Collection API; sample - configure webhook to push new utility consumption as it is posted.

3.10 DEXMA Energy Intelligence
• Feature/Setting: Data Sources Integration, DEXMA API; sample - automate periodic retrieval of historical or live data.

3.11 Enel X JuiceNet
• Feature/Setting: Data Export Webhook; sample - receive event-driven usage logs from charging sessions.

3.12 ChargePoint
• Feature/Setting: Data Export API; sample - schedule recurring exports of station energy usage.

3.13 OpenADR (Automated Demand Response)
• Feature/Setting: VEN (Virtual End Node) telemetry channels; sample - register VEN for periodic reports to aggregator.

3.14 GridPoint Energy Manager
• Feature/Setting: REST API - Meter Readings; sample - poll aggregated readings for dashboard update.

3.15 BuildingIQ
• Feature/Setting: Data Exchange (DX) interface; sample - create automation to pull usage for predictive analytics.

3.16 Zenner IoT Gateway
• Feature/Setting: MQTT Client feed; sample - configure device topics, subscribe automatically for all new measurements.

3.17 ThingSpeak
• Feature/Setting: REST Channels API (GET feeds); sample - schedule pulls of energy sensor channels by Site ID.

3.18 ARC Facilities
• Feature/Setting: Integration Webhooks; sample - trigger reports on new facility inspection logs affecting energy consumption.

3.19 BACnet/IP
• Feature/Setting: Automated polling of "Analog Value" object for kWh meters; schedule OPC client for routine data functions.

3.20 OSIsoft PI System
• Feature/Setting: AF SDK or PI Web API; sample - script automated historical and live data extraction for all fuel/electric meter tags.

Benefits

4.1. Ensures data accuracy and timeliness for compliance, cost tracking, and sustainability objectives.
4.2. Enables proactive management of energy usage anomalies, predictive maintenance, and efficiency improvements.
4.3. Reduces operational labor by eliminating manual meter reads and report assembly.
4.4. Provides real-time visibility into enterprise-wide consumption for trend analysis and optimization.
4.5. Facilitates seamless integration with analytics, billing, and regulatory reporting systems.