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HER Participation Model Draft Load Forecasting Manual Language Chuck AlongeDemand Forecasting & Analysis Installed Capacity Working Group January 26, 2023COPYRIGHTNYISO 2023. ALL RIGHTS RESERVEDAgendaCOPYRIGHTKey

How to fill out data-driven short-term load forecasting

How to fill out data-driven short-term load forecasting

1. Collect historical load data
2. Gather relevant weather data
3. Preprocess the data to handle missing values and outliers
4. Select a suitable forecasting model such as ARIMA or machine learning algorithms
5. Split the data into training and testing sets
6. Train the model on the training data
7. Evaluate the model using the testing data
8. Make forecasts for future load values

Who needs data-driven short-term load forecasting?

1. Utility companies for efficient energy distribution planning
2. Electricity market operators for pricing and infrastructure planning
3. Renewable energy providers for optimizing energy production and storage

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What is data-driven short-term load forecasting?

Data-driven short-term load forecasting is the process of predicting the future energy demand for a specific short time period using statistical and analytical methods based on historical data.

Who is required to file data-driven short-term load forecasting?

Utilities, energy suppliers, and grid operators that are responsible for managing electric load and maintaining balance in the energy market are typically required to file data-driven short-term load forecasting.

How to fill out data-driven short-term load forecasting?

To fill out data-driven short-term load forecasting, collect relevant historical data on energy consumption, analyze trends, specify the forecasting period, and present the data using established formats or templates provided by regulatory bodies.

What is the purpose of data-driven short-term load forecasting?

The purpose of data-driven short-term load forecasting is to enable power system operators to anticipate energy demand, optimize the generation of electricity, ensure reliability, and make informed decisions regarding resource allocation.

What information must be reported on data-driven short-term load forecasting?

Information that must be reported includes historical load data, forecasts for the upcoming periods, assumptions used for forecasting, methodology employed, and any factors that may influence demand.