scholarly journals Algorithm design for wind prediction in Berakit Bay, Bintan Island using Long Short-Term Memory (LSTM) method

2021 ◽  
Vol 944 (1) ◽  
pp. 012006
Author(s):  
D R Pratama ◽  
I Jaya ◽  
M Iqbal
Keyword(s):  
Wind Speed ◽  
Short Term Memory ◽  
Algorithm Design ◽  
Short Term ◽  
Average Wind Speed ◽  
Term Memory ◽  
Average Wind ◽  
Long Short Term Memory ◽  
System Designs

Abstract Wind speed is a crucial parameter alongside coastal areas, especially Indonesia. Above average wind speed can cause harmful effects on human activities. This study uses wind speed data from Berakit Bay, Bintan Island is a potential location for coastal community settlement, fisheries, and tourist activities. The wind parameter then predicted using the Long Short-Term Memory or LSTM algorithm. This algorithm is able to study long-term dependencies by converting simple nervous system designs into specialized blocks containing cells. It is suitable to be applied to long-term wind predictions where the wind speed at this time is very influential with the wind speed in the future. In preparing the LSTM, the data preprocessing and the architecture used will determine the prediction results. In this study, four different architectures were made in order to determine the most optimal architecture. The results show that the LSTM architecture is able to obtain a relatively good RMSE value of 1.87 and an accuracy of 39.40% with the use of two LSTM layers, 256 units in the first layer and 128 in the second layer. The LSTM algorithm in predicting wind can also be applied to other areas in Indonesia.

scholarly journals Analysis of Meteorological Factor Multivariate Models for Medium- and Long-Term Photovoltaic Solar Power Forecasting Using Long Short-Term Memory

2020 ◽  
Vol 11 (1) ◽  
pp. 316
Author(s):  
Namrye Son ◽  
Mina Jung
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Cloud Cover ◽  
Solar Power ◽  
Short Term Memory ◽  
Numerical Models ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Solar power generation is an increasingly popular renewable energy topic. Photovoltaic (PV) systems are installed on buildings to efficiently manage energy production and consumption. Because of its physical properties, electrical energy is produced and consumed simultaneously; therefore solar energy must be predicted accurately to maintain a stable power supply. To develop an efficient energy management system (EMS), 22 multivariate numerical models were constructed by combining solar radiation, sunlight, humidity, temperature, cloud cover, and wind speed. The performance of the models was compared by applying a modified version of the traditional long short-term memory (LSTM) approach. The experimental results showed that the six meteorological factors influence the solar power forecast regardless of the season. These are, from most to least important: solar radiation, sunlight, wind speed, temperature, cloud cover, and humidity. The models are rated for suitability to provide medium- and long-term solar power forecasts, and the modified LSTM demonstrates better performance than the traditional LSTM.

scholarly journals Air pollution forecasting application based on deep learning model and optimization algorithm

2021 ◽  
Author(s):  
Azim Heydari ◽  
Meysam Majidi Nezhad ◽  
Davide Astiaso Garcia ◽  
Farshid Keynia ◽  
Livio De Santoli
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Power Plant ◽  
Air Temperature ◽  
Short Term Memory ◽  
Combined Cycle ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

AbstractAir pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO2 and SO2 by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO2, and SO2 for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO2 and SO2); type (2) includes just lagged values of the output variables (NO2 and SO2). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables. Graphic abstract

Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network

2020 ◽  
Vol 213 ◽  
pp. 112869 ◽  
Author(s):  
Sinvaldo Rodrigues Moreno ◽  
Ramon Gomes da Silva ◽  
Viviana Cocco Mariani ◽  
Leandro dos Santos Coelho
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Wind Speed Forecasting ◽  
Decomposition Model ◽  
Multi Stage ◽  
Long Short Term Memory

scholarly journals Deep Learning for Wave Energy Converter Modeling Using Long Short Term Memory

2021 ◽  
Author(s):  
Seyed Milad Mousavi ◽  
Majid Ghasemi ◽  
Mahsa Dehghan Manshadi ◽  
Amir Mosavi
Keyword(s):  
Wind Speed ◽  
Output Power ◽  
Wave Energy ◽  
Short Term Memory ◽  
Ocean Waves ◽  
Wave Energy Converter ◽  
Energy Converter ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Accurate forecasts of ocean waves energy can not only reduce costs for investment but it is also essential for management and operation of electrical power. This paper presents an innovative approach based on the Long Short Term Memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analyzing is provided by collecting the experimental data from another study and the exerted data from numerical simulation of searaser. The simulation is done with Flow-3D software which has high capability in analyzing the fluid solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study the wind speed and output power are related with a LSTM method. Moreover, it can be inferred that the LSTM Network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement and the root mean square is 0.49 in the mean value related to the accuracy of LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of LSTM method.

scholarly journals Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

2018 ◽  
Vol 7 (4.15) ◽  
pp. 25 ◽  
Author(s):  
Said Jadid Abdulkadir ◽  
Hitham Alhussian ◽  
Muhammad Nazmi ◽  
Asim A Elsheikh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting models.  

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