2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series
Copyright policy )2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series
Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.
Technology, T, Multivariate prediction, deep learning, convolutional neural network, Convolutional neural network, Deep learning, multivariate prediction; deep learning; energy time series; convolutional neural network; long short-term memory network, Long short-term memory network, energy time series, multivariate prediction, Energy time series, Convolutional neural network; Deep learning; Energy time series; Long short-term memory network; Multivariate prediction, long short-term memory network
Technology, T, Multivariate prediction, deep learning, convolutional neural network, Convolutional neural network, Deep learning, multivariate prediction; deep learning; energy time series; convolutional neural network; long short-term memory network, Long short-term memory network, energy time series, multivariate prediction, Energy time series, Convolutional neural network; Deep learning; Energy time series; Long short-term memory network; Multivariate prediction, long short-term memory network
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