Non‐linear mixed‐effects models for time series forecasting of smart meter demand-Reference-Cited by-同舟云学术

Non‐linear mixed‐effects models for time series forecasting of smart meter demand

Published:2021-02-03 Issue:6 Volume:40 Page:1118-1130
ISSN:0277-6693
Container-title:Journal of Forecasting
language:en
Short-container-title:Journal of Forecasting

Author:

Roach Cameron¹^ORCID,Hyndman Rob¹,Ben Taieb Souhaib²

Affiliation:

1. Department of Econometrics and Business Statistics Monash University Clayton Victoria Australia

2. Department of Computer Science University of Mons Mons Belgium

Publisher

Wiley

Subject

Management Science and Operations Research,Statistics, Probability and Uncertainty,Strategy and Management,Computer Science Applications,Modeling and Simulation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.2750

Reference26 articles.

1. Workload forecasting for a call center: Methodology and a case study

2. Forecasting electricity smart meter data using conditional kernel density estimation

3. Bates D. Maechler M. Bolker B. &Walker S.(2019).lme4: Linear mixed‐effects models using ‘Eigen’ and S4.https://CRAN.R‐project.org/package=lme4 R package version 1.1‐21.

4. Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression

5. A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers

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