Short-Term Load Forecasting Using Random Forest with Entropy-Based Feature Selection

Subbiah, Siva Sankari; Jayakumar, C.

doi:10.1007/978-981-16-6448-9_8

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Deep learning methods can handle non-linear, seasonal, sequence-dependent air pollutant data effectively (Drewil andAl-Bahadili 2022, Subbiah andChinnappan 2020). The time series air pollutant data has the internal sequence dependency behavior between different pollutants.…”

Section: Deep Learningmentioning

confidence: 99%

Prediction of Particulate Matter PM2.5 Using Bidirectional Gated Recurrent Unit with Feature Selection

2024

Global NEST: The International Journal

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<p>In recent years, air pollution has increased with industrialization and urbanization globally. It is an important hazardous factor that causes severe health issues to community’s health. Among the number of pollutants in air, PM2.5 is very dangerous due to its very small, 2.5µm, diameter. The PM2.5 concentration in air causes severe life-threatening to humans. In this paper, RFBIGRU model is proposed to predict PM2.5 in the atmospheric air. RFBIGRU improves PM2.5 prediction accuracy using Random Forest (RF) feature selector and Bidirectional Gated Recurrent Unit (BIGRU) deep neural network. The PM2.5 concentration in air depends on other pollutants' concentration in the air. However, the consideration of several other pollutants increases the curse of dimensionality and overfitting issues. So, in RFBIGRU, first, the relevant pollutants to PM2.5 are identified using random forest feature importance. Then the nonlinear and temporal patterns of the time series air pollutant data are extracted both in forward and backward direction using Bidirectional GRU. The RFBIGRU reduces the curse of dimensionality, overfitting and improves the PM2.5 prediction accuracy compared to other deep learning methods. The experimental result proves RFBIGRU outperforms others by producing least Root Mean Square Error of 42.217 and 6.813 for Delhi and Amaravathi regions.</p>

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Section: Deep Learningmentioning

confidence: 99%

Prediction of Particulate Matter PM2.5 Using Bidirectional Gated Recurrent Unit with Feature Selection

2024

Global NEST: The International Journal

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“…The complexity of the learning model also increases when the size and dimension of data increase [27,28]. The irrelevant data given as input may confuse the learning model and increases the processing time [29]. To reduce the model complexity and to reduce the time taken for the learning process, the relevant features for the wind speed should be identified in advance [30].…”

Section: Feature Selectionmentioning

confidence: 99%

Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

Subbiah¹,

Paramasivan²,

Karmel³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Wind speed forecasting is important for wind energy forecasting. In the modern era, the increase in energy demand can be managed effectively by forecasting the wind speed accurately. The main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty, the curse of dimensionality, overfitting and non-linearity issues. The curse of dimensionality and overfitting issues are handled by using Boruta feature selection. The uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory (Bi-LSTM). In this paper, Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed forecasting. The model identifies relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection (BFS). Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time steps. The proposed BFS-Bi-LSTM model is compared against Multilayer perceptron (MLP), MLP with Boruta (BFS-MLP), Long Short Term Memory (LSTM), LSTM with Boruta (BFS-LSTM) and Bi-LSTM in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE) and R 2 . The BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784, MAE of 0.530, MSE of 0.615 and R 2 of 0.8766. The experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

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