Energy Forecasting: A Comprehensive Review of Techniques and Technologies

Aristeidis Mystakidis, Paraskevas Koukaras, Nikolaos Tsalikidis, Dimosthenis Ioannidis and Christos Tjortjis ()
Additional contact information
Aristeidis Mystakidis: School of Science and Technology, International Hellenic University, 14th km Thessaloniki-Moudania, 57001 Thessaloniki, Greece
Paraskevas Koukaras: School of Science and Technology, International Hellenic University, 14th km Thessaloniki-Moudania, 57001 Thessaloniki, Greece
Nikolaos Tsalikidis: Information Technologies Institute, Centre for Research & Technology, 57001 Thessaloniki, Greece
Dimosthenis Ioannidis: Information Technologies Institute, Centre for Research & Technology, 57001 Thessaloniki, Greece
Christos Tjortjis: School of Science and Technology, International Hellenic University, 14th km Thessaloniki-Moudania, 57001 Thessaloniki, Greece

Energies, 2024, vol. 17, issue 7, 1-33

Abstract: Distribution System Operators (DSOs) and Aggregators benefit from novel energy forecasting (EF) approaches. Improved forecasting accuracy may make it easier to deal with energy imbalances between generation and consumption. It also helps operations such as Demand Response Management (DRM) in Smart Grid (SG) architectures. For utilities, companies, and consumers to manage energy resources effectively and make educated decisions about energy generation and consumption, EF is essential. For many applications, such as Energy Load Forecasting (ELF), Energy Generation Forecasting (EGF), and grid stability, accurate EF is crucial. The state of the art in EF is examined in this literature review, emphasising cutting-edge forecasting techniques and technologies and their significance for the energy industry. It gives an overview of statistical, Machine Learning (ML)-based, and Deep Learning (DL)-based methods and their ensembles that form the basis of EF. Various time-series forecasting techniques are explored, including sequence-to-sequence, recursive, and direct forecasting. Furthermore, evaluation criteria are reported, namely, relative and absolute metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination ( R 2 ), and Coefficient of Variation of the Root Mean Square Error (CVRMSE), as well as the Execution Time (ET), which are used to gauge prediction accuracy. Finally, an overall step-by-step standard methodology often utilised in EF problems is presented.

Keywords: forecasting; time-series analysis; energy load; machine learning; artificial neural networks; statistical methods (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/7/1662/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/7/1662/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:7:p:1662-:d:1367464

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().