2017 28th Irish Signals and Systems Conference (ISSC) 2017
DOI: 10.1109/issc.2017.7983638
|View full text |Cite
|
Sign up to set email alerts
|

Adaptive sliding window load forecasting

Abstract: Small-scale, renewable generation which is embedded in the distribution network is causing previously unseen fluctuations in demand. In Northern Ireland this new generation, which is not visible to, or controllable by, the system operator, is presenting major challenges for accurate load forecasting. Currently deployed load forecasting methods are struggling to cope due to the rapid growth in this new generation, and its weather dependent nature. In this paper linear load forecasting methods are investigated w… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2018
2018
2022
2022

Publication Types

Select...
2
1

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 6 publications
0
1
0
Order By: Relevance
“…solar capacity, weather and calendar variables) to net demand variations. Foster et al investigated a similar strategy using a forward selection regression technique [9] for linear models and correlationbased approach [10] for nonlinear models to select the most appropriate variables. Wang et al proposed a method in [11] to model the conditional forecast residual and use it to improve the results of probabilistic forecasting methods.…”
Section: Introductionmentioning
confidence: 99%
“…solar capacity, weather and calendar variables) to net demand variations. Foster et al investigated a similar strategy using a forward selection regression technique [9] for linear models and correlationbased approach [10] for nonlinear models to select the most appropriate variables. Wang et al proposed a method in [11] to model the conditional forecast residual and use it to improve the results of probabilistic forecasting methods.…”
Section: Introductionmentioning
confidence: 99%