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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Promphak Boonraksa
Rajamangala University of Technology Suvarnabhumi
Computer Science & IT Engineering

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Deep learning-based integrated XAI for photovoltaic power forecasting considering actual power production period Promphak Boonraksa; Warunee Srisongkram; Kedsara Palachai; Boonruang Marungsri; Terapong Boonraksa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2970-2984

Abstract

This paper proposes a deep learning (DL)-based integrated explainable artificial intelligence (XAI) framework for photovoltaic (PV) power forecasting, explicitly considering the actual power production period to improve operational reliability. The framework uses solar irradiance, ambient temperature, and relative humidity as input features and evaluates nine DL architectures, including artificial neural networks (ANN), recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), CNN-LSTM, CNN-BiLSTM, RNN-LSTM, and RNN-BiLSTM. Model performance is evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results show that the residual-based RNN-LSTM model provides highest forecasting accuracy, achieving MAE of 1.21 kW, MAPE of 5.12%, and RMSE of 2.24 kW. In comparison, the LSTM and BiLSTM models exhibit substantially higher prediction errors, with MAPEs exceeding 21%, while hybrid convolutional models show moderate improvements but remain inferior. To enhance model transparency, XAI techniques are integrated to interpret feature contributions. The analysis confirms that solar irradiance is the dominant influencing factor, while temperature and humidity introduce secondary nonlinear effects captured effectively by recurrent architectures. The proposed framework provides a high-accuracy and interpretable solution for PV power forecasting, supporting reliable energy management and smart grid applications.
An explainable artificial intelligence benchmarking with deep reinforcement learning for telecom battery backup prediction Promphak Boonraksa; Kedsara Palachai; Veerapatra Wangsilabatra; Terapong Boonraksa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4482-4496

Abstract

Accurate prediction of battery backup time is essential for ensuring service reliability and efficient energy management in modern telecommunication networks, particularly during power outages. This study proposes a comprehensive benchmarking framework to evaluate the performance of eleven advanced time-series forecasting models, including deep learning architectures (multi-layer perceptron (MLP), convolutional neural network 1-dimension (CNN1D), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and temporal convolutional network (TCN)), Transformer-based models (Transformer, Informer-Lite, and patch time-series Transformer-lite (PatchTST-Lite)), and linear baseline approaches (decomposition linear (DLinear) and normalized linear (NLinear)). The models are trained and validated using real-world telecom power system data under varying load and environmental conditions. To improve model transparency and reliability, an explainable artificial intelligence (XAI) framework is incorporated to interpret feature contributions and identify key factors influencing battery performance. Experimental results indicate that Transformer-based models outperform conventional deep learning and linear models in terms of prediction accuracy and robustness across different operating scenarios. Furthermore, XAI analysis highlights that load current, ambient temperature, and battery health indicators are the most influential variables affecting backup time prediction. The proposed framework provides both high accuracy forecasting and interpretability, enabling more effective decision-making and enhancing energy resilience in telecommunication infrastructure.
Co-Authors Boonruang Marungsri Kedsara Palachai Kedsara Palachai Terapong Boonraksa Veerapatra Wangsilabatra Warunee Srisongkram