Mean metrics values—Mean squared error (MSE), mean absolute error (MAE) and coefficient of determination (R2) for history models sorted by MSE in validation set. [PDF]
, 2023 Mean metrics values—Mean squared error (MSE), mean absolute error (MAE) and coefficient of determination (R2) for history models sorted by MSE in validation set.
Tomasz Gutowski (17200042) +2 more
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JOIV: International Journal on Informatics Visualization, 2020 — After being introduced in 2008, the rise in the price of bitcoin and the popularity of other cryptocurrencies triggered a growing discussion about how much energy was consumed during the production of this currency.
Febri Liantoni, Arif Agusti
doaj +1 more source
A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm
JOIV: International Journal on Informatics Visualization, 2023 A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept.
Salam Abdulkhaleq Noaman +2 more
doaj +1 more source
Using the Mean Absolute Percentage Error for Regression Models [PDF]
CoRR, 2015 We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression.
de Myttenaere, Arnaud +3 more
openaire +3 more sources
Mean metrics values—Mean squared error (MSE), mean absolute error (MAE) and coefficient of determination (R2) for impulse models sorted by MSE in validation set. [PDF]
, 2023 Mean metrics values—Mean squared error (MSE), mean absolute error (MAE) and coefficient of determination (R2) for impulse models sorted by MSE in validation set.
Tomasz Gutowski (17200042) +2 more
core +1 more source
Load Forecasting Techniques for Power System: Research Challenges and Survey
IEEE Access, 2022 The main and pivot part of electric companies is the load forecasting. Decision-makers and think tank of power sectors should forecast the future need of electricity with large accuracy and small error to give uninterrupted and free of load shedding ...
Naqash Ahmad +3 more
doaj +1 more source
Mean-risk models using two risk measures: A multi-objective approach [PDF]
, 2006 This paper proposes a model for portfolio optimisation, in which distributions are characterised and compared on the basis of three statistics: the expected value, the variance and the CVaR at a specified confidence level.
Diana Roman +5 more
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Comparison of errors generated by different similarity selection thresholds under the same experimental conditions (mean absolute error and root mean square error on MovieLens with selected users). [PDF]
, 2022 Comparison of errors generated by different similarity selection thresholds under the same experimental conditions (mean absolute error and root mean square error on MovieLens with selected users).
Zhong Huang (358974) +5 more
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Mean absolute error (MAE; kcal·min-1) and Mean absolute percentage error of predicted PAEE using generated linear regression equations for each monitor at each location. [PDF]
, 2016 Mean absolute error (MAE; kcal·min-1) and Mean absolute percentage error of predicted PAEE using generated linear regression equations for each monitor at each location.
Tom Edward Nightingale (2213017) +3 more
core +1 more source
Average Absolute Error and Root Mean Square Error for the reproduction number and the latent intensity in scenario C. [PDF]
, 2023 Average Absolute Error and Root Mean Square Error for the reproduction number and the latent intensity in scenario C.
Stamatina Lamprinakou (14700944) +2 more
core +1 more source