Indonesian load prediction estimation using long short term  memory

Yuniarti, Erliza; Nurmaini, Siti; Suprapto, Bhakti Yudho

doi:10.11591/ijai.v11.i3.pp1026-1032

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Int J Artif Intell ISSN: 2252-8938  Yuniarti et al [24] predicted the electrical load of Indonesian power grid using 1 to 3 layers of LSTM for comparison. Based on an hourly historical data between 2013 to 2017, they were able to predict the electrical load demand for the next 24 hours.…”

Section: Related Workmentioning

confidence: 99%

Predicting tidal level in tropical Eastern Bintan waters using residual long short-term memory

Syakti,

Rhamadhan,

Laziola

et al. 2024

IJ-AI

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<span lang="EN-US">The sea brings many benefits for society, especially for a maritime country such as Indonesia. The potential in various sectors is limited only by the willingness of a party to invest in it. One such investment is in learning the knowledge and information that can be gathered from the sea, and even predicting its behavior with enough data. Using a residual LSTM algorithm, we will predict the tidal level in eastern Bintan island, a tropical island on the tip of Malay peninsula. The dataset is acquired from two sensor points in eastern Bintan coast from July 2018 to June 2019 for a span of one year, giving a total of 7,961 data points. The residual LSTM model consists of a residual wrapper with two consecutive LSTM layers and one dense layer. The model is also compared with variations of LSTM and RNN models. The result of the residual LSTM model has an MAE value of 0.1495 cm and an RMSE value of 0.3353 cm, compared to the baseline model’s 1.1148 cm and 1.4107 cm respectively. The model also has an RMSE value improvement of 76.23% compared to the base model.</span>

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Section: Related Workmentioning

confidence: 99%

Predicting tidal level in tropical Eastern Bintan waters using residual long short-term memory

Syakti,

Rhamadhan,

Laziola

et al. 2024

IJ-AI

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“…These technologies have contributed to eliminating many of the difficulties in the different tasks including time series analysis (TSA) and forecasting, which helps us to predict the future values of a data series using its historical values. TSA is a crucial area for research in several fields [1]- [5]. In the financial field, TSA can be used for forecasting instrument prices to help investors and researchers to understand and beat market fluctuations.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid deep learning model for price prediction

Abdullah

Salah

2023

IJECE

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Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several models’ architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNN-LSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models.

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Indonesian load prediction estimation using long short term memory

Cited by 2 publications

References 22 publications

Predicting tidal level in tropical Eastern Bintan waters using residual long short-term memory

Predicting tidal level in tropical Eastern Bintan waters using residual long short-term memory

A novel hybrid deep learning model for price prediction

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