TABLE IV: Performance comparisons across data selection methods for Gaussian Processes Method MAPE R2 score σ2pred 95% Data selection plus training time (s) Closest Time Selection 2.67 0.91 6626 93.62 0.13 Closest Distance Selection 2.1 0.94 3507 90.1 62 MAP Selection 2.02 0.94 3992 94.73 165...
The weighted linear error correction method chooses the MAPE as objective function, employing the IGWO-JAYA algorithm to determine the optimal weights for the point forecasting value and residual forecasting value. By multiplying these values by their respective weights and summing them, the accuracy ...
Models are compared using absolute error (AE), maximum absolute error (MAXAE), mean absolute error (MAE), mean bias error (MBE), mean squared error (MSE), absolute percentage error (APE), mean absolute percentage error (MAPE), standard deviation of absolute error (StdAE), standard ...
Additionally, compared to earlier research utilizing two well-known public data sets, the MAPE is optimized by 2.2% and 5%, respectively. Also, the method has good prediction accuracy for a wide variety of time granularities and load aggregation levels, so it can be applied to various load ...
The data-preprocessing method based on MCM makes the forecasting results closer to the actual load than those without process, which reduces the Mean Absolute Percentage Error (MAPE) of load prediction from 11.54% to 10.92%. Furthermore, through sensitivity analysis, it was found that among the ...
The assessment of prediction performance involves the computation of metrics, such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), to measure the disparity between the predicted values and the ground truth. (6...
They offer a one day ahead forecast based on the load values of the previous 4 days, presenting a MAPE result of 2.13. A similar application of DMD is carried out in [40] but applying DMD to predict forecast errors followed by an extreme value constraint method to further correct the ...
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The accuracy and efficiency of the model is analyzed using statistical parameters like MAE, MAPE and RMSE. Based on the experimental results ELM based load forecasting gives better accuracy when compared to the traditional NN based algorithm. 展开 关键词: Load forecasting Load modeling Smart grids ...
To conduct this research, datasets from each model were analyzed using various statistical indices, including R2, RMSE, RMSLE, MAPE, BF, AARD, NSE, U 95 , t-stat, TIC, and PI. The results indicated that the Bi-LSTM model demonstrated superior performance for heating ...