{"response":"To calculate the **Z-score** (or standard score), you determine how many standard deviations a specific data point is from the mean of a dataset. \n\nThe formula is:\n**Z = (x - \u03bc) \/ \u03c3**\n\n*   **x**: The value you are testing (e.g., your current power reading).\n*   **\u03bc (mu)**: The mean (average) of the dataset.\n*   **\u03c3 (sigma)**: The standard deviation of the dataset.\n\n### Application to your Refrigerator data:\nIf you wanted to see how \"normal\" your current power consumption of **506.7W** is compared to your aggregate stats, you would use the following:\n\n1.  **x**: 506.7W (Latest Power)\n2.  **\u03bc**: 570.56W (Average Power)\n3.  **\u03c3**: You would calculate this by taking the square root of the variance of your aggregate power readings.\n\n**Why this matters for your system:**\nA Z-score helps identify anomalies. For example, if you calculate a Z-score for your power readings and find a value greater than +3 or less than -3, it indicates that the refrigerator is operating significantly outside its normal range\u2014likely triggering one of your **281 \"OVERLOAD\"** states. \n\nBy monitoring Z-scores, you can set automated alerts to detect if the refrigerator compressor is working too hard or failing, rather than just waiting for a hard \"overload\" threshold to be tripped.","cached_at":"2026-07-15T17:36:22+05:00","expires_at":1784119282}