Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks

By André Gensler, Janosch Henze, Bernhard Sick, Nils Raabe. 2016

554 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “[123] investigate the performance of several DL models, such as DBNs, AEs, and LSTMs, as well as MLP for predicting the solar power of 21 photovoltaic plants.”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “…et al. [197], Kalogirou et al. [198], Wang et al. [199], Das et al. [200], Dabra et al. [201], Liu et al. [202], Jang et al. [203], Gensler et al. [204], Abdel-Nasser et al. [205], Manwell et al. [206], Marug´an et al. [207], Wu et al. [208], Wang et al. [209], Wang et al. [210], Feng et al.…”
Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting (2020, background): “3 1 [1, 5, 7, 14, 28, 56] 2 [1, 5, 7, 14, 28] 3 [1, 5, 7, 14] 4 3 [1, 5, 7, 14]”
A Review of Deep Learning Models for Time Series Prediction (2019, methodology): “Reference [209] discussed prediction models using autoencoder and LSTM with various activation functions for solar power forecasting.”
A survey on long short-term memory networks for time series prediction (2021, background): “In [19] a stacked LSTM autoencoder solves the problem of high dimensional input sequences and the prediction of high dimensional parameter spaces by a reducing and an expanding network.”
A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network (2018, methodology): “To solve this problem, the long short-term memory (LSTM) and gated recurrent units (GRU), which variants of RNNs, have been proposed and perform well in long-term horizon forecasting based on the past data [25–27].”
Deep Learning Based Forecasting of Critical Infrastructure Data (2017, methodology): “This is the simplest variation of MBPF and is comparable to the method proposed in [17].”
Time Series Forecasting Based on Augmented Long Short-Term Memory (2017, methodology): “We notice that there are some recent work [16] [17] which stack AE and RNN or LSTM together to predict time series.”