Oracle 1Z0-1091-22 Exam Dumps

Oracle Utilities Meter Data Management normalizes measurements during the Create the Final Measurements stage of loading interval initial measurement data. Normalization is the process of converting interval measurements into a common format that can be processed by Oracle Utilities Meter Data Management. Normalization involves storing each interval measurement as a single record for a specific date and time, regardless of how they are received from smart meter systems.

Oracle Utilities Meter Data Management does not normalize measurements during other stages of loading interval initial measurement data, such as Load the Initial Measurements stage or Validation, Estimation, and Editing (VEE) stage. Normalization is not done for other purposes, such as validation or intervalization.

Asset and component specifications are used to describe design details and asset attributes that are common to a group of assets or components. According to the Oracle Utilities Meter Solution Cloud Service Business User Guide, some examples of smart meter specifications are:

Firmware: The software version installed on the smart meter

Asset type: The category of the smart meter, such as electric, gas, water, or heat

Manufacturer: The company that produced the smart meter

Dynamic aggregation is a feature that allows users to perform aggregation processes on usage data based on dynamic queries that are defined by administrative data. Dynamic aggregation can be used to generate aggregated usage data for different dimensions and criteria, such as customer class, rate schedule, or geographic area. According to the Oracle Utilities Meter Data Management Business User Guide, some examples of the main components of dynamic aggregation are:

Measuring Component Sets: These define the dimensions and criteria by which aggregation is performed. Measuring Component Sets specify which characteristics and values should be used to group usage data for aggregation purposes.

Measuring Components: These represent ''buckets'' or unique combinations of dimension values that are generated by Measuring Component Sets. Measuring Components store the aggregated usage data for each combination of dimension values.

Data sources: These store project-provided SQL for getting data from different sources, such as usage subscriptions, measuring components, or virtual meters. Data sources specify which data should be used as input for aggregation processes.

Derived values are values that are calculated from raw measurements based on certain rules or factors. Derived values can be used for different purposes, such as billing, analysis, or reporting. Some examples of derived values are:

Value with a factor such as line loss applied: This is a value that is adjusted by applying a factor that accounts for the loss of energy or water during transmission or distribution. For example, a line loss factor can be applied to a meter reading to calculate the actual amount of energy or water that was delivered to a customer.

Value converted from one unit of measurement to another: This is a value that is converted from one unit of measurement (UOM) to another based on a conversion factor. For example, a volume reading in cubic feet can be converted to a volume reading in gallons by multiplying it by a conversion factor.

Interval data values created by ''intervalizing'' a scalar reading by applying a profile to it: This is a value that is created by dividing a scalar reading into smaller time intervals based on a profile that represents the usage pattern of a customer. For example, a daily scalar reading can be intervalized into hourly readings by applying a load profile that reflects the customer's hourly usage.

Estimated value if the final measurement is too low or high is not a derived value, but an estimated value. Estimated values are values that are generated when raw measurements are missing or invalid based on certain criteria. Estimated values can be based on historical data, statistical methods, or other sources.

Comparison of normal versus actual usage is not a derived value, but an analysis result. Analysis results are values that are calculated by comparing or aggregating measurements or derived values for different purposes, such as revenue protection, load research, or customer engagement.

The interval proxy day estimation VEE rule is a rule that estimates missing intervals by selecting interval data to average from a list of days that are most like the day being estimated. This is achieved by measuring the component comparison periods, which are periods of time that are used to compare different days based on certain criteria. According to the Oracle Utilities Meter Data Management Business User Guide, one factor that is used to define the proxy or the list of days that are most like the day being estimated is:

Weather data: This is data that indicates the temperature or other weather conditions for a given day. Weather data can be used to select days that have similar weather patterns or variations as the day being estimated.