%0 Conference Paper %1 8489399 %A Koprinska, Irena %A Wu, Dengsong %A Wang, Zheng %B 2018 International Joint Conference on Neural Networks (IJCNN) %D 2018 %K CNN forecasting sem_wise2223 series time %P 1-8 %R 10.1109/IJCNN.2018.8489399 %T Convolutional Neural Networks for Energy Time Series Forecasting %U https://ieeexplore.ieee.org/document/8489399 %X We investigate the application of convolutional neural networks for energy time series forecasting. In particular, we consider predicting the photovoltaic solar power and electricity load for the next day, from previous solar power and electricity loads. We compare the performance of convolutional neural networks with multilayer perceptron neural networks, which are one of the most popular and successful methods used for these tasks, and also with long short-term memory recurrent neural networks and a persistence baseline. The evaluation is conducted using four solar and electricity time series from three countries. Our results showed that the convolutional and multilayer perceptron neural networks performed similarly in terms of accuracy and training time, and outperformed the other models. This highlights the potential of convolutional neural networks for energy time series forecasting.

@inproceedings{8489399, abstract = {We investigate the application of convolutional neural networks for energy time series forecasting. In particular, we consider predicting the photovoltaic solar power and electricity load for the next day, from previous solar power and electricity loads. We compare the performance of convolutional neural networks with multilayer perceptron neural networks, which are one of the most popular and successful methods used for these tasks, and also with long short-term memory recurrent neural networks and a persistence baseline. The evaluation is conducted using four solar and electricity time series from three countries. Our results showed that the convolutional and multilayer perceptron neural networks performed similarly in terms of accuracy and training time, and outperformed the other models. This highlights the potential of convolutional neural networks for energy time series forecasting.}, added-at = {2022-09-13T19:28:08.000+0200}, author = {Koprinska, Irena and Wu, Dengsong and Wang, Zheng}, biburl = {https://www.bibsonomy.org/bibtex/249d375e86c583c7c11a4874280c514dd/annakrause}, booktitle = {2018 International Joint Conference on Neural Networks (IJCNN)}, description = {Convolutional Neural Networks for Energy Time Series Forecasting | IEEE Conference Publication | IEEE Xplore}, doi = {10.1109/IJCNN.2018.8489399}, interhash = {a75d7788488689992d06663d6bb19eaa}, intrahash = {49d375e86c583c7c11a4874280c514dd}, issn = {2161-4407}, keywords = {CNN forecasting sem_wise2223 series time}, month = {July}, pages = {1-8}, timestamp = {2022-09-14T08:18:55.000+0200}, title = {Convolutional Neural Networks for Energy Time Series Forecasting}, url = {https://ieeexplore.ieee.org/document/8489399}, year = 2018 }