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ChatGPT Answers: What is the typical ratio between non-technical and technical losses in a distribution utility?

What is the typical ratio between non-technical and technical losses in a distribution utility? The typical ratio between non-technical and technical losses in a distribution utility can vary depending on a range of factors such as the location of the utility, the age and condition of the distribution system, and the regulatory environment. In general, however, technical losses tend to be higher than non-technical losses. Technical losses are caused by the physical characteristics of the electric distribution system, such as resistance in transmission and distribution lines and equipment inefficiencies, and can account for up to 8-15% of total energy losses in a distribution utility. Non-technical losses, on the other hand, are caused by theft and fraud, meter inaccuracies, and billing errors and can account for up to 5-7% of total energy losses in a distribution utility. However, it's important to note that the ratio between technical and non-technical losses can vary significantl

ChatGPT Answers: How billing timing difference affects systems loss data?

How billing timing difference affects systems loss data? Billing timing differences can have an impact on power system loss data, especially if the data is being used for analysis or performance monitoring purposes. This is because billing data is typically collected at different times than system loss data, and these differences can affect the accuracy and reliability of the data. One potential issue is that billing data is often collected on a monthly or quarterly basis, whereas system loss data may be recorded at a higher frequency, such as every 15 minutes or hourly. This means that the billing data may not accurately reflect changes in power consumption or losses that occur on a more granular time scale. Another potential issue is that billing data is usually based on customer meter readings, which are affected by a range of factors, such as meter accuracy, measurement errors, and tampering. This can result in inaccuracies in the billing data, which can in turn affect the accuracy