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About Deep Learning For Time Series Modeling
This document summarizes a project using deep learning models to forecast energy loads. It tested various deep learning architectures including feedforward neural networks, recurrent neural networks, and convolutional neural networks. The best performing model was a recurrent neural network with an RMSE of 530 kWh/h. Feature engineering improved performance, but allowing the neural network to learn nonlinear relationships directly worked best. Anomaly filtering and Fourier transforms did not significantly improve results. The project aimed to develop models that approach the accuracy of private sector energy forecasting tools.
Detailed Information
Author: | ['Felipe Angel'] |
---|---|
Publication Year: | 2015 |
Pages: | 5 |
Language: | English |
Format: | |
Price: | FREE |
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