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Forecasting
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Energy Forecasting Using Time-Series Analysis
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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: closed (30 June 2023) | Viewed by 5004

Special Issue Editors

Dr. Xiaojun Luo

E-Mail Website
Guest Editor
Department of Accounting, Economics and Finance, Faculty of Business and Law, University of the West of England (UWE), Bristol BS16 1QY, UK
Interests: artificial intelligence; blockchain; machine learning; building energy modeling; building information modeling; sustainable buildings; renewable and sustainable energy
Special Issues, Collections and Topics in MDPI journals
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 submissions that pass pre-check are 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 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.

Keywords

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

Published Papers (1 paper)

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Research

29 pages, 7505 KiB  
Article
Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production
by Ashish Sedai, Rabin Dhakal, Shishir Gautam, Anibesh Dhamala, Argenis Bilbao, Qin Wang, Adam Wigington and Suhas Pol
Forecasting 2023, 5(1), 256-284; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast5010014 - 22 Feb 2023
Cited by 7 | Viewed by 4107
Abstract
The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-term forecasting of renewable energy resources using an existing ML/DL model is still debatable [...] Read more.
The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-term forecasting of renewable energy resources using an existing ML/DL model is still debatable and needs additional research. Considering the constraints inherent in current empirical or physical-based forecasting models, the study utilizes ML/DL models to provide long-term predictions for solar power production. This study aims to examine the efficacy of several existing forecasting models. The study suggests approaches to enhance the accuracy of long-term forecasting of solar power generation for a case study power plant. It summarizes and compares the statistical model (ARIMA), ML model (SVR), DL models (LSTM, GRU, etc.), and ensemble models (RF, hybrid) with respect to long-term prediction. The performances of the univariate and multivariate models are summarized and compared based on their ability to accurately predict solar power generation for the next 1, 3, 5, and 15 days for a 100-kW solar power plant in Lubbock, TX, USA. Conclusions are drawn predicting the accuracy of various model changes with variation in the prediction time frame and input variables. In summary, the Random Forest model predicted long-term solar power generation with 50% better accuracy over the univariate statistical model and 10% better accuracy over multivariate ML/DL models. Full article
(This article belongs to the Special Issue Energy Forecasting Using Time-Series Analysis)
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