New Forecasting Metrics Evaluated in Prophet, Random Forest, and Long Short-Term Memory Models for Load Forecasting

New Forecasting Metrics Evaluated in Prophet, Random Forest, and Long Short-Term Memory Models for Load Forecasting

Data mining is vital for smart grids because it enhances overall grid efficiency, enabling the analysis of large volumes of data, the optimization of energy distribution, the identification of patterns, and demand forecasting. Several performance metrics, such as the MAPE and RMSE, have been created...

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Bibliographic Details
Main Authors: Prajowal Manandhar, Hasan Rafiq, Edwin Rodriguez-Ubinas, Themis Palpanas
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Energies
Subjects:
smart grid
load forecasting
machine learning
deep learning
time series
performance metrics
Online Access:https://www.mdpi.com/1996-1073/17/23/6131
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