RMSE and MAPE analysis for short-term solar irradiance, solar energy, and load forecasting using a Recurrent Artificial Neural Network

Kumar, Nilesh; Saravanan, D.; Kumar, Labh; Shukla, Pradum; Shaw, Rabindra Nath

doi:10.1016/b978-0-323-91699-8.00010-3

Cited by 9 publications

(9 citation statements)

References 17 publications

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“…The evaluation index objectively compares the prediction results of the proposed model with those of other predictive models. Therefore, the RMSE [52], mean absolute error (MAE) [53], mean absolute percentage error (MAPE) [54], R-Squared (R 2 ) [55], Explainable variance (Evar) [56], mean squared logarithmic error (MSLE) [57], and median absolute error (MedAE) [58] are used as model performance evaluation indicators, and the specific calculation method is shown in equations ( 13)- (19), 16)…”

Section: The Proposed Model Prediction Results Evaluation Indexmentioning

confidence: 99%

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Zhou,

Xiao,

et al. 2024

Meas. Sci. Technol.

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Accurate tool wear monitoring is crucial for increasing tool life and machining productivity. Although many prediction models can achieve high prediction accuracy, there are problems such as poor stability in the face of different working conditions or tool signals. A tool wear prediction method based on improved deep extreme learning machines (DELM) was proposed as a solution to this issue; it uses the sparrow search algorithm (SSA) to upgrade the input weight of DELM to improve the model, and then extracts the time-domain, frequency-domain, and time-frequency domain characteristics from multi-sensor signals to construct and test the improved model SSA-DELM. The verification results show that the proposed model accurately reflects the tool wear. Meanwhile, the RMSE of the proposed model decreased by 53.39%, 19.95%, 43.86%, 23.80%, 24.80%, and 3.72%, respectively, and the MAE decreased by 67.81%, 17.87%, 32.70%, 29.90%, 30.30%, and 6.78%, respectively, compared to the with unimproved DELM, PSO-LSSVM, LSTM, SSAE, RNN, and DBO-DELM.

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Section: The Proposed Model Prediction Results Evaluation Indexmentioning

confidence: 99%

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Zhou,

Xiao,

et al. 2024

Meas. Sci. Technol.

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“…Comparing the prediction results in Figure 2, it can be seen that the overall prediction trend and inflection point of the prediction curve of this method have higher accuracy and are closer to the actual load curve. This article uses a predictive model to evaluate the deviation between the predicted and actual results using commonly used RMSE (Root Mean Square Error) and MAPE (mean absolute percentage error) [12] indicators. The predictive evaluation index values of each method are shown in Table 1.…”

Section: Comparative Analysis Of Intraday Load Forecasting Results Ba...mentioning

confidence: 99%

Short term load forecasting method based on local shape similarity and SVR

Jiao,

Wang,

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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This article proposes a short-term load forecasting method based on local shape similarity and SVR. This method first calculates the local shape similarity coefficient between the predicted daily load and historical samples, and selects historical samples with smaller shape similarity coefficients as similar days, solving the problem of neglecting the shape information similarity of load series in traditional similar day selection. By calculating the weight of the impact of similar days on the prediction results and the weighted load change rate of historical time points, the intraday prediction of the load series at the predicted points is achieved. In addition, the intraday known point data and the predicted data of the predicted points are concatenated as input to the SVR prediction model, so that the prediction model takes into account the intraday latest load data changes and overcomes the problem of not considering the intraday load changes in the next day's prediction. The experimental results show that the proposed approach can improve the accuracy of shortterm load forecasting and reduce prediction errors, which has certain practical value.

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“…Untuk menguji keakuratan hasil perhitungan Prediksi, digunakan Mean Absolute Precentage Error (MAPE). MAPE merupakan perhitungan yang digunakan untu mengukur kesalahan dengan menghitung persentase perbedaan antara data real dengan data Prediksi, rumus MAPE, terdapat pada persamaan 8 [12], [13]. Nilai MAPE semakin kecil maka hasilnya semakin akurat sebuah model peramalan…”

Section: 𝑧 =unclassified

Penerapan Fuzzy Tsukamoto untuk Sistem Prediksi Jumlah Produksi Roti Berbasis Web

Nurhayati,

Irmayanti,

Wijaya

2024

KOMPUTA

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This research aims to create a system that will provide information about predicting the amount of production to help related parties in determining the amount of production, in addition to seeing the accuracy of the use of methods in predicting the amount of production. Production activities are a series of activities carried out to turn raw materials into products that can be marketed and sold. One of the things involved in this process is the amount of raw materials that will be made into finished products. A calculation process is needed to predict the number of products to be produced to minimize production costs. The method used for the prediction process is the Fuzzy Tsukamoto method, where this method overcomes uncertainty and complexity in decision making. Calculation of errors in predicting is used mean absolute percentage error (MAPE), and for system design an object approach is used using Unified Modeling Language (UML) tools. The results of the study state that system testing is worth 92%, meaning that the system can be used as needed, and for the method used to get an error value of 10.62% so that the Fuzzy Tsukamoto method can be used to predict the amount of production.

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RMSE and MAPE analysis for short-term solar irradiance, solar energy, and load forecasting using a Recurrent Artificial Neural Network

Cited by 9 publications

References 17 publications

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Short term load forecasting method based on local shape similarity and SVR

Penerapan Fuzzy Tsukamoto untuk Sistem Prediksi Jumlah Produksi Roti Berbasis Web

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