Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data

Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data

The heat load in district heating systems is affected by the weather and by human behavior, and special consumption patterns are observed around holidays. This study employs a top-down approach to heat load forecasting using meteorological data and new untraditional data types such as school holiday...

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Bibliographic Details
Main Authors: Magnus Dahl, Adam Brun, Oliver S. Kirsebom, Gorm B. Andresen
Format: Article
Language:English
Published: MDPI AG 2018-06-01
Series:Energies
Subjects:
district heating
load forecasting
machine learning
weather data
consumer behavior
neural networks
support vector machines
Online Access:http://www.mdpi.com/1996-1073/11/7/1678

Internet

http://www.mdpi.com/1996-1073/11/7/1678

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