Faculty Publications
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Publications by NITK Faculty
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Item Solar irradiation forecast enhancement using clustering based CNN-BiLSTM-attention hybrid architecture with PSO(Taylor and Francis Ltd., 2024) Chiranjeevi, M.; Madyastha, A.; Maurya, A.K.; Moger, T.; Jena, D.Accurate solar irradiation forecasting is essential for optimising solar energy use. This paper presents a novel forecasting approach: the ‘Clustering-based CNN-BiLSTM-Attention Hybrid Architecture with PSO’. It combines clustering, attention mechanisms, Convolutional Neural Networks (CNN), Bidirectional Long-Short Term Memory (BiLSTM) networks, and Particle Swarm Optimisation (PSO) into a unified framework. Clustering categorises days into groups, improving predictive capabilities. The CNN-BiLSTM model captures spatial and temporal features, identifying complex patterns. PSO optimises the hybrid model’s hyperparameters, while an attention mechanism assigns probability weights to relevant information, enhancing performance. By leveraging spatial and temporal patterns in solar data, the proposed model improves forecasting accuracy in univariate and multivariate analyses with multi-step predictions. Extensive tests on real-world datasets from various locations show the model’s effectiveness. For example, with NASA power data, the model achieves a Mean Absolute Error (MAE) of 24.028 W/m2, Root Mean Square Error (RMSE) of 43.025 W/m2, and an R2 score of 0.984 for 1-hour ahead forecasting. The results show significant improvements over conventional methods. © 2024 Informa UK Limited, trading as Taylor & Francis Group.Item Solar Irradiance Forecasting Performance Enhancement Using Hybrid Fuzzy-Based CNN-BiLSTM-Transformer Model(Institute of Electrical and Electronics Engineers Inc., 2025) Chiranjeevi, M.; Moger, T.; Jena, D.Accurate forecasting of solar irradiance plays a vital role in optimizing solar energy utilization, but it remains a challenging task due to high variability and uncertainty caused by fluctuating atmospheric conditions. Traditional forecasting techniques often fail to capture nonlinear patterns and long-term dependencies effectively, leading to reduced prediction accuracy. Although recent advancements in deep learning have shown superior performance in time series forecasting, their integration with fuzzy time series (FTS) methods has been relatively unexplored. To bridge this gap, this article introduces an innovative FTS-based forecasting framework that integrates deep learning with fuzzy modeling to overcome these limitations. The proposed model combines Convolutional Neural Networks, Bidirectional Long Short-Term Memory, and Transformer architecture (CNN-BiLSTM-Transformer) with a fuzzy model employing Gaussian membership functions to process historical solar irradiance data. This approach enables the model to generate accurate forecasts while managing both first-order and high-order fuzzy relations. Additionally, the Sine Cosine Optimization algorithm is used to fine-tune the model’s hyperparameters, further enhancing its performance. The effectiveness of the model is validated through experiments using real-world solar irradiance datasets collected from three different websites for Mangalore location. The results demonstrate that the proposed model achieves a Mean Absolute Error (MAE) of 21.805 W/m2, a Root Mean Square Error (RMSE) of 93.089 W/m2, and an R2 score of 0.981 for one-step-ahead forecasting using NREL data, outperforming the performance of state-of-the-art methods and highlighting its effectiveness in solar irradiance forecasting. © 2013 IEEE.