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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES)
Allias, Noormadinah
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Economics, Econometrics & Finance

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Analysing feature selection: impacts towards forecasting electricity power consumption Malik, Azman Ab; Tao, Lyu; Allias, Noormadinah; Hamzah, Irni Hamiza
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp265-272

Abstract

This study focuses on the development of electrical power forecasting based on electricity usage in Wuzhou, China. To develop a forecasting model, the important features need to be identified. Therefore, this study investigates the performance of the feature selection method, focusing on the mutual information as a filter and random forest as a wrapper-based feature selection. From the experiment, six features have been chosen, whereby both feature selection methods chose almost identical features. Later, the selected features are trained and tested with common machine learning models, namely random forest regressor, support vector regression (SVR), k-nearest neighbor (KNN) regressor, and extreme gradient boosting (XGBoost) regressor. The performances of the feature selections tested on each of the models are measured in terms of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R²). Findings from the experiment revealed that XGBoost outperform the other machine learning models with RMSE 0.9566 and R² indicated with 0.2561. However, SVR outperformed XGBoost and other model by obtaining MAE 0.6028. It can be concluded that the performance of filter-based outperformed the embedded feature selection.
Investigating the performance of RNN model to forecast the electricity power consumption in Guangzhou China Mingying, Han; Ab Malik, Azman; Allias, Noormadinah; Hamzah, Irni Hamiza binti
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp497-506

Abstract

The project initiatives to create a reliable prediction model for power loads in Guangzhou, China. The power industry is facing issues due to rapid market growth and the necessity for better grid management, prompting this response. In developing the models, conventional machine learning models have been used so far, but in this study, the performance of deep learning is investigated. Therefore, the recurrent neural network (RNN) was selected for the prediction of electricity consumption. Later, the performance of the model was compared with autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), and RNN. The experimental results show that the RNN outperforms ARIMA and LSTM, with an R² value of 0.92, an RMSE of 0.13107 and an MAE of 0.0176. The project improved power resource planning and management, selected an acceptable forecasting model RNN and contributed to forecasting technology developments. The study identified limits in historical data availability and quality, as well as external influences affecting the studies. RNN models can help optimize resource allocation and improve energy planning.
Co-Authors Ab Malik, Azman Hamzah, Irni Hamiza Hamzah, Irni Hamiza binti Malik, Azman Ab Mingying, Han Tao, Lyu