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How to Inspect Measurement Data with the alitiq Load API 🔍

The load forecasting API provides powerful tools to inspect and analyze the historical measurement data for your assets/portfolios. This feature allows you to validate submitted data, track system performance, and gain insights into your load characteristics.

Overview

Inspecting data involves retrieving historical measurement records stored in the alitiq system for a specific load timeseries. You can define a time range, customize the output format, and perform detailed analysis on the retrieved data.

Key Features ✨

  • Flexible Time Ranges: Retrieve data for any specified date range.
  • Detailed Records: Includes power output, timestamps, and optional irradiance values.
  • Analysis Ready: Data is returned in a format suitable for direct analysis using tools like pandas.

Required Parameters

To inspect data, provide the following information:

Parameter Type Description Default
location_id str Unique identifier of the location whose data you want to inspect. None
start_date datetime (Optional) Start date for the inspection range. 2 days before today
end_date datetime (Optional) End date for the inspection range. Today

Example: Inspect Data

Below is an example of how to use the measurement/inspect endpoint to inspect data and the method get_measurements: 

import requests

url = "https://engine.alitiq.com/forecast/single"

querystring = {"location_id":"123","response_format":"json"}

payload = ""
headers = {"x-api-key": "api-key"}

response = requests.request("GET", url, data=payload, headers=headers, params=querystring)

print(response.text)

from datetime import datetime, timedelta
from alitiq import alitiqLoadAPI

# Initialize the API client
load_api = alitiqLoadAPI(api_key="your-api-key")

# Define the location and date range
location_id = "99"  # defined by alitiq
start_date = datetime.now() - timedelta(days=7)  # 7 days ago
end_date = datetime.now()  # Today

# Inspect measurement data
data = load_api.get_measurements(
    location_id=location_id, 
    start_date=start_date, 
    end_date=end_date
)

# Print the retrieved data
print(data)

curl --request GET \
  --url 'https://engine.alitiq.com/measurement/inspect?location_id=123&response_format=json' \
  --header 'x-api-key: api-key'

API Response

The API returns the measurement data in a pandas-compatible format (e.g., JSON), which can be directly loaded into a DataFrame for further analysis: 

|       dt        |   power   |  timezone | interval_in_minutes | window_boundary |  
|-----------------|-----------|-----------|---------------------|------------------|  
| 2024-06-10 10:00|  120.5    |   UTC     | 15                  | end              |  
| 2024-06-10 10:15|  90.8     |   UTC     | 15                  | end              |  
| 2024-06-10 10:30|  150.0    |   UTC     | 15                  | end              |

Best Practices

  • Validate Data: Regularly inspect your measurement data to ensure it is accurate and complete.
  • Batch Analysis: Retrieve data in chunks for longer periods to avoid API response size limitations.
  • Timezone Awareness: Ensure the timezone is consistent across measurements for accurate analysis.

Advanced Use Case: Data Visualization

You can use tools like matplotlib or seaborn to visualize the data for insights: 

import matplotlib.pyplot as plt

# Visualize power output over time
data.plot(x='dt', y='power', title="Power Output Over Time", figsize=(10, 6))
plt.show()

Notes

  • Performance: For large datasets, limit the date range to improve response time.
  • Missing Data: If any data points are missing, double-check your measurement submissions.
  • Data Validation: The SDK validates data upon submission to minimize errors during inspection.

For further questions or issues, contact support@alitiq.com. 🌟