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Review

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

1
Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Brasil 2950, Valparaíso, Chile
2
Escuela de Ingeniería C. Biomédica, Universidad de Valparaíso, Chacabuco 2092-2220, Valparaíso, Chile
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(12), 1412; https://doi.org/10.3390/e22121412
Received: 29 October 2020 / Revised: 9 December 2020 / Accepted: 10 December 2020 / Published: 15 December 2020
(This article belongs to the Special Issue Time Series Modelling)
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. View Full-Text
Keywords: electric power; forecasting accuracy; machine learning electric power; forecasting accuracy; machine learning
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MDPI and ACS Style

Vivas, E.; Allende-Cid, H.; Salas, R. A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score. Entropy 2020, 22, 1412. https://doi.org/10.3390/e22121412

AMA Style

Vivas E, Allende-Cid H, Salas R. A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score. Entropy. 2020; 22(12):1412. https://doi.org/10.3390/e22121412

Chicago/Turabian Style

Vivas, Eliana, Héctor Allende-Cid, and Rodrigo Salas. 2020. "A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score" Entropy 22, no. 12: 1412. https://doi.org/10.3390/e22121412

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