Wind Speed Prediction Using Deep Learning-LSTM and GRU

Kumar, V. Sanjay; Nookesh, V. Mallikarjuna; Saketh, B. Satya; Syama, S; Ramprabhakar, J.

doi:10.1109/icosec51865.2021.9591886

Cited by 11 publications

(2 citation statements)

References 22 publications

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“…On the other hand, LSTM layers use three gates: forget, input, and output, with the same objective. Available studies compare GRU and LSTM performances in RNNs for different applications, for instance: forecasting, 80 cryptocurrency, 81,82 wind speed, 83,84 condition of a paper press, 85 motive classication in thematic apperception tests 86 and music and raw speech. 87 Nevertheless, it is not clear which of those layers would perform better at a given task.…”

Section: Gated Layermentioning

confidence: 99%

Predicting small molecules solubility on endpoint devices using deep ensemble neural networks

Ramos,

White

2024

Digital Discovery

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Section: Gated Layermentioning

confidence: 99%

Predicting small molecules solubility on endpoint devices using deep ensemble neural networks

Ramos,

White

2024

Digital Discovery

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show abstract

“…Known as modified RNN, GRU has outperformed in many fields and has proven in better performance compared to traditional RNN [38]. GRU demonstrated its superiority in forecasting such as wind power forecasting [39], aquaculture [40], air quality [41] and etc. GRU were also applied in predicting financial time series data.…”

Section: Introductionmentioning

confidence: 99%

Heteroscedasticity effects as component to future stock market predictions using RNN-based models

Sadon,

Ismail,

Khamis

et al. 2024

PLoS ONE

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Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices.

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An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems

Syama,

Ramprabhakar,

Anand

et al. 2024

Sci Rep

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The current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates for the adoption of renewable energy sources (RES) but also efficiently schedules all conventional power generation units to balance the increasing load demand while simultaneously minimizing fuel costs and harmful emissions that are currently addressed by Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) problem solutions. However, the integration of renewable energy resources (RES) further complicates the UC-CEED problem due to their intermittent nature. Recently, metaheuristic algorithms are acquiring momentum in resolving constrained UC-CEED problems due to their improved global solution ability, adaptability, and derivative-free construction. In this research, a computationally efficient binary hybrid version of crow search algorithm and improvised grey wolf optimization is proposed, namely Crow Search Improved Binary Grey Wolf Optimization Algorithm (CS-BIGWO) by inclusion of nonlinear control parameter, weight-based position updating, and mutation approach. Statistical results on standard mathematical functions prove the supremacy of the proposed algorithm over conventional algorithms. Further, a novel optimization strategy is devised by integrating enhanced lambda iteration with the CS-BIGWO algorithm (CS-BIGWO- ) to solve a day-ahead UC-CEED problem of the hybrid energy system incorporating cost functions of RES. For the model, a day-ahead forecast of wind power and solar photovoltaic power is obtained by using the Levy-Flight Chaotic Whale Optimization Algorithm optimized Extreme Learning Machines(LCWOA-ELM). The proposed algorithm is tested for the UC-CEED solution of an IEEE-39 bus system with two distinct cases: (1) without RES integration and (2) with RES integration. Several independent trial runs are executed, and the performance of the algorithms is assessed based on optimal UC schedules, fuel cost, emission quantization, convergence curve, and computational time. For case 1, the proposed algorithm resulted in a percentage reduction of 0.1021% in fuel cost and 0.7995% in emission. In contrast, for test case 2, it resulted in a percentage reduction of 0.12896% in fuel cost and 0.772% in emission with the proposed algorithm. The results validate the dominance of the proposed methodology over existing methods in terms of lower fuel costs and emissions.

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Wind Speed Prediction Using Deep Learning-LSTM and GRU

Cited by 11 publications

References 22 publications

Predicting small molecules solubility on endpoint devices using deep ensemble neural networks

Predicting small molecules solubility on endpoint devices using deep ensemble neural networks

Heteroscedasticity effects as component to future stock market predictions using RNN-based models

An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid energy systems

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