https://github.com/rootfs/grafana_dashboard_forecasting_integration
https://github.com/rootfs/grafana_dashboard_forecasting_integration
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/rootfs/grafana_dashboard_forecasting_integration
- Owner: rootfs
- Created: 2025-03-04T00:54:38.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-04T01:00:10.000Z (3 months ago)
- Last Synced: 2025-03-04T01:30:33.419Z (3 months ago)
- Language: Jupyter Notebook
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Grafana Forecasting Integration
This project provides a forecasting service that integrates with Grafana to display energy demand forecasts. It uses historical data to generate forecasts using ARIMA models and exposes them through a JSON API that Grafana can consume.
## Features
- Time series forecasting using ARIMA models
- Grafana JSON API integration
- Fallback mechanisms for robustness
- Configurable time ranges and regions
## Prerequisites
- Python 3.7+
- Grafana 8.0+ with the Grafana JSON Datasource plugin installed
## Installation
1. Clone this repository
2. Install the required Python packages:
```
pip install -r requirements.txt
```
3. Install the Grafana JSON Datasource plugin
## Usage
```bash
python forecasting.py
```
The service will start on port 9001 by default. You can configure the port using the `API_PORT` environment variable.
### Configuring Grafana
1. Open Grafana (default: http://localhost:3000)
2. Go to Configuration > Data Sources
3. Add a new data source of type "Grafana JSON Datasource"
4. Set the URL to http://localhost:9001/forecast (or wherever your service is running)
5. Save and test the data source
6. Import the dashboard by going to Create > Import
7. Upload the `grafana_dashboard.json` file or paste its contents
8. Select the JSON data source you created and click Import
## Configuration
The following environment variables can be used to configure the service:
- `API_PORT`: Port for the Grafana JSON API (default: 9001)
## Data Sources
By default, the service uses a mock data source with sample data from a CSV file:
1. Place a CSV file named `carbon_intensity.csv` in the same directory as the script
2. The CSV should have a `start_date` column and columns for each region (e.g., `ISONE`, `CAISO`, etc.)
## Adding More Regions
To add more regions for forecasting:
1. Edit the `self.regions` list in the `ForecastService` class
2. Add the region data to your CSV file if using sample data
## Integrating with Kepler and Green Dashboard
This is a diagram of the integration of the forecasting service with Kepler and the Green Dashboard.
```mermaid
graph TD;
A[Forecast Model] -->|Generates JSON| B[FastAPI Server];
B -->|Exposes /forecast Endpoint| C[Grafana JSON API Plugin];
C -->|Queries JSON Data| D[Green Dashboard];
E[Kepler Prometheus Metrics] -->|Real Power Consumption| D;
```