Grid Search of Convolutional Neural Network model in the case of load forecasting

• Autorzy: Tran Thanh Ngoc

Identyfikatory

DOI

10.24425/aee.2021.136050

• Języki publikacji: EN

Abstrakty

EN

The Convolutional Neural Network (CNN) model is one of the most effective models for load forecasting with hyperparameters which can be used not only to determine the CNN structure and but also to train the CNN model. This paper proposes a frame work for Grid Search hyperparameters of the CNN model. In a training process, the optimalmodels will specify conditions that satisfy requirement for minimum of accuracy scoresof Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). In the testing process, these optimal models will be used to evaluate the results along with all other ones. The results indicated that the optimal models have accuracy scores near the minimum values. Load demand data of Queensland (Australia) and Ho Chi Minh City (Vietnam) were utilized to verify the accuracy and reliability of the Grid Search framework.

Słowa kluczowe

EN

load forecasting; grid search; convolutional neural network

PL

prognozowanie obciążenia; wyszukiwanie w siatce; konwolucyjna sieć neuronowa

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2021
• Tom: Vol. 70, nr 1
• Strony: 25--36
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wz.

Twórcy

autor

Tran Thanh Ngoc

• Faculty of Electrical Engineering Technology, Industrial University of Ho Chi Minh City12 Nguyen Van Bao, Ward 4, Go Vap District, Ho Chi Minh City, Vietnam

• https://orcid.org/0000-0001-8757-3714

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).