Forecasting
A Practitioner’s Guide to Short-term Load Forecast Modeling
December 05, 2018
Over the years, numerous clients have requested a “recipe book” for building powerful short-term load forecast models. This guide to Short-term Load Forecast Modeling is a partial “recipe book,” providing the full list of possible ingredients with guidance as to when to use which combination of ingredients. The focus is on building the within-day and day-ahead load forecast models that system operators and energy traders rely on for scheduling, dispatching and procuring generation to meet demand. The information presented is based on 20 plus years of experience forecasting in the trenches with system operators and energy traders in Australia, Europe and North America.
The guide begins with the hard work of data review and analysis. In practice, the path to a powerful forecast model is through a very thorough analysis of the data. The first section outlines an approach for reviewing load data. This is followed by data cleaning approaches and philosophies. With the preliminaries complete, the Like Day, Multivariate Regression and Neural Network load forecast techniques that are the bread and butter of the industry are introduced, and the discussion includes descriptions of machine learning frameworks that can be used to complement today’s operational load forecast tools. Next is defining a set of explanatory variables that can be used in a load forecast model, including the treatment of calendar conditions, holidays and weather conditions. A series of load forecast model recipes and associated model building guidelines are introduced. The guide finishes with basic concepts related to incorporating behind-the-meter solar generation into a load forecast model.
Download the Guide: A Practitioner’s Guide to Short-term Load Forecast Modeling
The guide begins with the hard work of data review and analysis. In practice, the path to a powerful forecast model is through a very thorough analysis of the data. The first section outlines an approach for reviewing load data. This is followed by data cleaning approaches and philosophies. With the preliminaries complete, the Like Day, Multivariate Regression and Neural Network load forecast techniques that are the bread and butter of the industry are introduced, and the discussion includes descriptions of machine learning frameworks that can be used to complement today’s operational load forecast tools. Next is defining a set of explanatory variables that can be used in a load forecast model, including the treatment of calendar conditions, holidays and weather conditions. A series of load forecast model recipes and associated model building guidelines are introduced. The guide finishes with basic concepts related to incorporating behind-the-meter solar generation into a load forecast model.
Download the Guide: A Practitioner’s Guide to Short-term Load Forecast Modeling
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