2011 IEEE Power and Energy Society General Meeting 2011
DOI: 10.1109/pes.2011.6038881
|View full text |Cite
|
Sign up to set email alerts
|

A Naïve multiple linear regression benchmark for short term load forecasting

Abstract: Benchmarking issue in short term load forecasting has not received as much attention as it deserves. Although dozens of techniques have been reported to be applied to short term load forecasting, most of them are still on the theoretical level with insignificant practical value. None of them has been established to produce benchmarking models for comparative assessment. This paper proposes a naïve multiple linear regression benchmark for short term load forecasting, which is from the experience of helping a US… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
55
0
1

Year Published

2012
2012
2022
2022

Publication Types

Select...
6
2
1

Relationship

0
9

Authors

Journals

citations
Cited by 93 publications
(56 citation statements)
references
References 12 publications
0
55
0
1
Order By: Relevance
“…Thus, the multiple linear regression model (also Vanilla Model in [11]) is utilized as an outliers detector in the second stage. This method is firstly proposed and proved to be effective in [21].…”
Section: Outliers Detectionmentioning
confidence: 99%
“…Thus, the multiple linear regression model (also Vanilla Model in [11]) is utilized as an outliers detector in the second stage. This method is firstly proposed and proved to be effective in [21].…”
Section: Outliers Detectionmentioning
confidence: 99%
“…In practice, polynomials are frequently used to depict the nonlinear relationships between load and weather conditions [50, 51,52]. Two recent representative regression based short-term load models [4,42] are considered as benchmarks to show the competitive performance of TWE in capturing the nonlinear relationship between temperature forecast and load, while attaining a narrower band of prediction errors.…”
Section: Important Load Forecasting Factorsmentioning
confidence: 99%
“…The autoregressive moving average 45 on daily load patterns, as well as the temperature effect on load in [32]. 51 Huang and Shih presented a modifi univariate ARMA short-term load 52 model by considering a non-Gaussian process in [33]. Taylor fi established 53 a univariate time series load model which considered within-day and within-54 week seasonality by using exponential smoothing in [34].…”
mentioning
confidence: 99%
“…This work presented in the paper by Hong et al [54]. Chapter 2 of [53] presents a broad review of short term load forecasting techniques where the two main stochastic methods identified by the author are regression or time series analysis and in the artificial intelligence domain Artificial Neural Networks (ANN), fuzzy logic and Support Vector Machine (SVM) methods are mentioned.…”
Section: Models Of Demandmentioning
confidence: 99%