Presentation
Random Hyper-parameter Search-Based Deep Neural Network for Power Consumption Forecasting
Author/s | Torres, J. F.
Gutiérrez Avilés, David ![]() ![]() ![]() ![]() ![]() ![]() ![]() Troncoso Lora, Alicia Martínez Álvarez, Francisco |
Department | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Publication Date | 2019 |
Deposit Date | 2022-04-06 |
Published in |
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ISBN/ISSN | 978-3-030-20520-1 0302-9743 |
Abstract | In this paper, we introduce a deep learning approach, based on feed-forward
neural networks, for big data time series forecasting with arbitrary prediction horizons.
We firstly propose a random search to tune the multiple ... In this paper, we introduce a deep learning approach, based on feed-forward neural networks, for big data time series forecasting with arbitrary prediction horizons. We firstly propose a random search to tune the multiple hyper-parameters involved in the method perfor-mance. There is a twofold objective for this search: firstly, to improve the forecasts and, secondly, to decrease the learning time. Next, we pro-pose a procedure based on moving averages to smooth the predictions obtained by the different models considered for each value of the pre-diction horizon. We conduct a comprehensive evaluation using a real-world dataset composed of electricity consumption in Spain, evaluating accuracy and comparing the performance of the proposed deep learning with a grid search and a random search without applying smoothing. Reported results show that a random search produces competitive accu-racy results generating a smaller number of models, and the smoothing process reduces the forecasting error. |
Funding agencies | Ministerio de Economía y Competitividad (MINECO). España |
Project ID. | TIN2017-88209-C2-1-R
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Citation | Torres, J.F., Gutiérrez Avilés, D., Troncoso, A. y Martínez Álvarez, F. (2019). Random Hyper-parameter Search-Based Deep Neural Network for Power Consumption Forecasting. En IWANN 2019 : 15th International Work-Conference on Artificial Neural Networks (259-269), Gran Canaria, España: Springer. |
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