MLS-C01 · Question #186
A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is a
The correct answer is C. Convolutional Neural Network - Quantile Regression (CNN-QR). CNN-QR and DeepAR accepts related time series data (weather data, number of people on property, etc.,) https://docs.aws.amazon.com/forecast/latest/dg/aws-forecast-choosing-recipes.html
Question
A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays. The data scientists are using Amazon Forecast to generate the forecasts. Which algorithm in Forecast should the data scientists use to meet these requirements?
Options
- AAutoregressive Integrated Moving Average (AIRMA)
- BExponential Smoothing (ETS)
- CConvolutional Neural Network - Quantile Regression (CNN-QR)
- DProphet
How the community answered
(22 responses)- A5% (1)
- B5% (1)
- C82% (18)
- D9% (2)
Explanation
CNN-QR and DeepAR accepts related time series data (weather data, number of people on property, etc.,) https://docs.aws.amazon.com/forecast/latest/dg/aws-forecast-choosing-recipes.html
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