Energy Forecasting Using Time-Series Analysis

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

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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 semimonthly 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 1800 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.