Abstract
Combining the strengths of different modelling approaches
and various
information sources is studied in short-term forecasting
of aggregated electrical
loads that are controllable and include e.g. thermal
storage capacity. Measurement
data driven models tend to fail in forecasting power
during rare situations
such as dynamic control actions and extreme weather
conditions. The thermal
dynamics of the loads, large outdoor temperature
variations, and changes in
the technologies contribute to this challenge. Here we
study a model integration
approach using field trial data covering about 7000
houses and 27 months. Control
responses and load saturation are forecast using a
physically based structure.
The residual is forecast with a machine learning model
designed and tuned to
learn also system dynamics. The load forecast is the sum
of these component
forecasts. The forecasting accuracy of this hybrid method
is compared with using
the machine learning alone. The results show improvement
in the accuracy
Original language | English |
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Title of host publication | Proceedings ITISE 2017. Granada, 18-20, September, 2017. |
Editors | Olga Valenzuela, Ignacio Rojas |
Publisher | University of Granada |
Pages | 795-806 |
Volume | 2 |
ISBN (Print) | 978-84-17293-01-7 |
Publication status | Published - 2017 |
MoE publication type | A4 Article in a conference publication |
Event | International Work-Conference on TIme SEries Analysis, ITISE 2017 - Granada, Spain Duration: 18 Sept 2017 → 20 Sept 2017 |
Conference
Conference | International Work-Conference on TIme SEries Analysis, ITISE 2017 |
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Abbreviated title | ITISE 2017 |
Country/Territory | Spain |
City | Granada |
Period | 18/09/17 → 20/09/17 |
Keywords
- forecasting
- machine learning
- physically based models
- smart grid