Cargando…
A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score
Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7765272/ https://www.ncbi.nlm.nih.gov/pubmed/33333829 http://dx.doi.org/10.3390/e22121412 |
_version_ | 1783628452899848192 |
---|---|
author | Vivas, Eliana Allende-Cid, Héctor Salas, Rodrigo |
author_facet | Vivas, Eliana Allende-Cid, Héctor Salas, Rodrigo |
author_sort | Vivas, Eliana |
collection | PubMed |
description | Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years. |
format | Online Article Text |
id | pubmed-7765272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77652722021-02-24 A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score Vivas, Eliana Allende-Cid, Héctor Salas, Rodrigo Entropy (Basel) Review Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years. MDPI 2020-12-15 /pmc/articles/PMC7765272/ /pubmed/33333829 http://dx.doi.org/10.3390/e22121412 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Vivas, Eliana Allende-Cid, Héctor Salas, Rodrigo A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score |
title | A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score |
title_full | A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score |
title_fullStr | A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score |
title_full_unstemmed | A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score |
title_short | A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score |
title_sort | systematic review of statistical and machine learning methods for electrical power forecasting with reported mape score |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7765272/ https://www.ncbi.nlm.nih.gov/pubmed/33333829 http://dx.doi.org/10.3390/e22121412 |
work_keys_str_mv | AT vivaseliana asystematicreviewofstatisticalandmachinelearningmethodsforelectricalpowerforecastingwithreportedmapescore AT allendecidhector asystematicreviewofstatisticalandmachinelearningmethodsforelectricalpowerforecastingwithreportedmapescore AT salasrodrigo asystematicreviewofstatisticalandmachinelearningmethodsforelectricalpowerforecastingwithreportedmapescore AT vivaseliana systematicreviewofstatisticalandmachinelearningmethodsforelectricalpowerforecastingwithreportedmapescore AT allendecidhector systematicreviewofstatisticalandmachinelearningmethodsforelectricalpowerforecastingwithreportedmapescore AT salasrodrigo systematicreviewofstatisticalandmachinelearningmethodsforelectricalpowerforecastingwithreportedmapescore |