Special Issue "Energy Forecasting Using Time-Series Analysis"

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Power and Energy Forecasting".

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editors

Dr. Xiaojun Luo
E-Mail Website
Guest Editor
Big-Data Enterprise and Artificial Intelligence Laboratory, Bristol Business School, University of the West of England, Bristol BS16 1QY, UK
Interests: artificial intelligence; machine learning; building energy management; multi-energy systems; life-cycle analysis; zero-carbon building
Prof. Dr. Paras Mandal
E-Mail Website
Guest Editor
Power & Renewable Energy Systems (PRES) Lab., Department of Electrical & Computer Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA
Interests: energy forecasting; cyber-physical systems; smart grid; power systems operations and control; machine learning; intelligent systems; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Time-series analysis for forecasting has recently become a widely investigated topic. The accuracy, effectiveness, repeatability, and computational time of forecasting algorithms are receiving increasing attention. Their applications are also varied, ranging from commercial buildings and industrial buildings to residential buildings; from single buildings and regional districts to nation-wide; from short-term and medium-term to long-term prediction; and from heating and cooling to electrical energy. Accurate and effective energy prediction is an important task to enhance energy efficiency and to plan and operate systems in a more reliable manner. Therefore, it is of great significance to develop and implement new intelligent, adaptive, accurate, effective, and time-saving energy prediction models. Big data, machine learning (ML), and artificial intelligence (AI) techniques have become critical to achieve time-series analysis and prediction.

This Special Issue aims to contribute to the advancement of energy prediction using intelligent, adaptive, accurate, effective, and time-saving time-series models. We invite papers on innovative time-series analysis applications to energy forecasting, including reviews and case studies.

Dr. Xiaojun Luo
Prof. Dr. Paras Mandal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • time-series
  • artificial intelligence
  • machine learning
  • deep learning
  • energy consumption forecast
  • renewable energy
  • building energy
  • smart grid

Published Papers

This special issue is now open for submission.
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