Faculty Publications

Now showing 1 - 3 of 3

Solar Irradiation Prediction Hybrid Framework Using Regularized Convolutional BiLSTM-Based Autoencoder Approach
(Institute of Electrical and Electronics Engineers Inc., 2023) Chiranjeevi, M.; Karlamangal, S.; Moger, T.; Jena, D.
Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task complex and difficult. To address the shortcomings of the traditional approaches, this research developed a hybrid resilient architecture for an enhanced solar irradiation forecast by employing a long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and the Bi-directional Long Short Term Memory (BiLSTM) model with grid search optimization. The suggested hybrid technique is comprised of two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM. The encoder is tasked with extracting the key features in order to deduce the input into a compact latent representation. The decoder network then predicts solar irradiance by analyzing the encoded representation's attributes. The experiments are conducted on three publicly available data sets collected from Desert Knowledge Australia Solar Centre (DKASC), National Solar Radiation Database (NSRDB), and Hawaii Space Exploration Analog and Simulation (HI-SEAS) Habitat. The analysis of univariate and multivariate-multi step ahead forecasting performed independently and it is compared with the conventional approaches. Several benchmark forecasting models and three performance metrics are utilized to validate the hybrid approach's prediction performance. The results show that the proposed architecture outperforms benchmark models in accuracy. © 2013 IEEE.
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.
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.

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