Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems-Reference-Cited by-同舟云学术

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Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems

Published:2019-02 Issue: Volume:45 Page:460-473
ISSN:2210-6707
Container-title:Sustainable Cities and Society
language:en
Short-container-title:Sustainable Cities and Society

Author:

Ahmad Tanveer,Chen Huanxin

Funder

‘National Natural Science Foundation of China’

Publisher

Elsevier BV

Subject

Transportation,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering,Geography, Planning and Development

Reference49 articles.

1. Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment;Ahmad;Energy,2018

2. Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches;Ahmad;Energy and Buildings,2018

3. Utility companies strategy for short-term energy demand forecasting using machine learning based models;Ahmad;Sustainable Cities and Society,2018

4. A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review;Ahmad;Energy and Buildings,2018

5. Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment;Ahmad;Energy,2018

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