Sequence to sequence deep learning models for solar irradiation forecasting

Mukhoty, Bhaskar; Maurya, Vikas; Shukla, Sandeep K.

doi:10.1109/ptc.2019.8810645

Cited by 23 publications

(13 citation statements)

References 14 publications

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“…As a result, some researches introduced encoder-decoder architecture based models for MTSF, which consists of two RNNs for feature encoding and decoding respectively. For example, Mukhoty et al [36] used two LSTM as the encoder and the decoder respectively to build a seq2seq model for multi-step solar irradiation forecasting and got excellent prediction accuracy. Liang et al [37] demonstrated the superiority of the encoder-decoder architecture over single RNN by comparison experiments on two environment quality datasets.…”

Section: A Related Workmentioning

confidence: 99%

A Hybrid Method With Adaptive Sub-Series Clustering and Attention-Based Stacked Residual LSTMs for Multivariate Time Series Forecasting

Liu

Cai

2020

IEEE Access

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Multivariate Time Series Forecasting (MTSF) has recently emerged its growing importance in many industries. However, how to reduce the influence of the noise components existing in time series on prediction and extract features effectively are still two challenges in MTSF. This paper focuses on those two challenges and proposes a new prediction method based on decomposition-ensemble framework called adaptive sub-series clustering-stacked residual LSTMs-multi-level attention mechanism (ASC-SRLSTMs-MLAttn). The method consists of three stages: decomposition, prediction, and ensemble. In the decomposition stage, the target series is decomposed by Ensemble Empirical Mode Decomposition into multiple sub-series, which will be clustered and reconstructed by the ASC algorithm to reduce the complexity and the time consumption of prediction. In the prediction stage, the sub-series and correlation series will be fed into SRLSTMs-MLAttn for sub-series prediction. The model is based on the encoder-decoder architecture with stacked residual LSTMs as the encoder, which can effectively capture the dependencies among multi variables and the temporal features from multivariate time series. Besides, a multi-level attention mechanism (MLAttn), which makes full use of the encoding information of the encoder, has been introduced to further improve the prediction performance of the model. In the ensemble stage, the predicted values of each sub-series will be summed to obtain the final prediction of the target time series. We also demonstrate the superiority and effectiveness of our proposed method on four public datasets via the conducted comparison experiment and ablation study. INDEX TERMS Multivariate time series forecasting, decomposition-ensemble framework, adaptive sub-series clustering algorithm, stacked residual long short-term memory, multi-level attention mechanism.

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Section: A Related Workmentioning

confidence: 99%

A Hybrid Method With Adaptive Sub-Series Clustering and Attention-Based Stacked Residual LSTMs for Multivariate Time Series Forecasting

Liu

Cai

2020

IEEE Access

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“…For sequence modeling problems, Seq2Seq (Sutskever et al, 2014) is the canonical deep learning framework and although applied this architecture to neural machine translation (NMT) tasks, it has since been adapted to time series forecasting (Nascimento et al, 2019;Yu et al, 2017;Gasparin et al, 2019;Mukhoty et al, 2019;Wen et al, 2017;Salinas et al, 2020;Wen and Torkkola, 2019). The MQ-Forecaster framework (Wen et al, 2017) solves (1) above by treating each series i as a sample from a joint stochastic process and feeding into a neural network which predicts Q quantiles for each horizon.…”

Section: Time Series Forecastingmentioning

confidence: 99%

“…Recent work applying deep learning to time-series forecasting focuses primarily on the use of recurrent and convolutional architectures (Nascimento et al, 2019;Yu et al, 2017;Gasparin et al, 2019;Mukhoty et al, 2019;Wen et al, 2017) 1 . These are Seq2Seq architectures (Sutskever et al, 2014) -which consist of an encoder which takes an input sequence and summarizes it into a fixed-length context vector, and a decoder which produces an output sequence.…”

Section: Introductionmentioning

confidence: 99%

MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention

Eisenach¹,

Patel²,

Madeka³

2020

Preprint

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Recent advances in neural forecasting have produced major improvements in accuracy for probabilistic demand prediction. In this work, we propose novel improvements to the current state of the art by incorporating changes inspired by recent advances in Transformer architectures for Natural Language Processing. We develop a novel decoder-encoder attention for contextalignment, improving forecasting accuracy by allowing the network to study its own history based on the context for which it is producing a forecast. We also present a novel positional encoding that allows the neural network to learn context-dependent seasonality functions as well as arbitrary holiday distances. Finally we show that the current state of the art MQ-Forecaster (Wen et al., 2017) models display excess variability by failing to leverage previous errors in the forecast to improve accuracy. We propose a novel decoder-self attention scheme for forecasting that produces significant improvements in the excess variation of the forecast.

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“…Sequence to sequence (S2S) DL models were used to forecast 24-h ahead of GHI. The results revealed superiority of LSTM over FFNN and gradient boosted regression trees algorithms [18]. The method can generally be implemented easily as it requires only historical GHI data.…”

Section: Introductionmentioning

confidence: 95%

Deep Neural Networks for Predicting Solar Radiation at Hail Region, Saudi Arabia

et al. 2021

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Forecasted global horizontal irradiation (GHI) can help for designing, sizing and performances analysis of photovoltaic (PV) systems including water PV pumping systems used for irrigation applications. In this paper, various deep neural networks (DNN) models for one day-ahead prediction of GHI at Hail city (Saudi Arabia) are developed and investigated. The considered DNN models include long-shortterm memory (LSTM), bidirectional-LSTM (BiLSTM), gated recurrent unit (GRU), bidirectional-GRU (Bi-GRU), one-dimensional convolutional neural network (CNN 1D ) and other hybrid configurations such as CNN-LSTM and CNN-BiLSTM. A dataset of daily GHI recordings collected during January 1, 2000 to June 30, 2020 from National Aeronautics and Space Administration (NASA) at an arid location (Hail, Saudi Arabia) is used to develop and compare the above DNN-based models. The parameters affecting the accuracy of the models have been also deeply analyzed. Only historical values of daily GHI have been used to build the DNN-based models whereas additional weather parameters such as air temperature, wind speed, wind direction, atmospheric pressure and relative humidity are not considered in this work. Keras library and Python language have been used to develop and compare the GHI forecasting models. The evaluation metrics such as correlation coefficient (r), Mean Absolute Percent Error (MAPE), Mean Absolute Error (MAE), cumulative distribution function (CDF) and standard deviation (σ ) are opted to evaluate the performance of the prediction models. The obtained results showed that the DNN models have provided globally good performances with a maximum reached value of r = 96%, for daily GHI forecasting.

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Sequence to sequence deep learning models for solar irradiation forecasting

Cited by 23 publications

References 14 publications

A Hybrid Method With Adaptive Sub-Series Clustering and Attention-Based Stacked Residual LSTMs for Multivariate Time Series Forecasting

A Hybrid Method With Adaptive Sub-Series Clustering and Attention-Based Stacked Residual LSTMs for Multivariate Time Series Forecasting

MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware Attention

Deep Neural Networks for Predicting Solar Radiation at Hail Region, Saudi Arabia

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