Faculty Publications
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Publications by NITK Faculty
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Item Solar Radiation Assessment and Forecasting Using Satellite Data(Springer Nature, 2019) Masoom, A.; Kashyap, Y.; Bansal, A.Since the availability of ground data is very sparse, satellite data provides an alternative method to estimate solar irradiation. Satellite data across various spectral bands may be employed to distinguish weather signatures, such as dust, aerosols, fog, and clouds. For a tropical country like India, which is potentially rich in solar energy resources, the study of these parameters is of crucial importance from the perspective of solar energy. Furthermore, a complete utilization of the solar energy depends on its proper integration with power grids. Because of its variable nature, incorporation of photovoltaic energy into electricity grids suffers technical challenges. Solar radiation is subjected to reflection, scattering and absorption by air molecules, clouds, and aerosols in the atmosphere. Clouds can block most of the direct radiation. Modern solar energy forecasting systems rely on real-time Earth observation from the satellite for detecting clouds and aerosols. © 2019, Springer Nature Singapore Pte Ltd.Item Machine Learning and Thresholding Approach for Defects Classification in Solar Panels(Springer Science and Business Media Deutschland GmbH, 2025) Abhishek, G.H.; Kumar, A.; Kashyap, Y.This research addresses critical aspects of solar photovoltaic (PV) system maintenance and monitoring to ensure sustained performance. Emphasizing solar panel reliability, the study employs image processing, clustering algorithms, and machine learning (K-Means, Naive Bayes) to detect and categorize factors impacting efficiency, such as dust accumulation and sunlight exposure. The developed system facilitates comprehensive assessment and classification, enhancing operational lifespan. Demonstrating versatility, the project incorporates alternative feature extraction and interactive threshold selection, ensuring adaptability to diverse scenarios. Experimental validation, including hotspot detection in thermal images, underscores the robustness of the proposed methodology, contributing significantly to solar panel monitoring and maintenance advancements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach(MDPI, 2023) Kumar, A.; Kashyap, Y.; Kosmopoulos, P.The rapid expansion of solar industries presents unknown technological challenges. A dedicated and suitable energy forecast is an effective solution for the daily dispatching and production of the electricity grid. The traditional forecast technique uses weather and plant parameters as the model information. Nevertheless, these are insufficient to consider problematic weather variability and the various plant characteristics in the actual field. Considering the above facts and inspired by the excellent implementation of the multi-column convolutional neural network (MCNN) in image processing, we developed a novel approach for forecasting solar energy by transforming multipoint time series (MT) into images for the MCNN to examine. We first processed the data to convert the time series solar energy into image matrices. We observed that the MCNN showed a preeminent response under a ground-based high-resolution spatial–temporal image matrix with a 0.2826% and 0.5826% RMSE for 15 min-ahead forecast under clear (CR) and cloudy (CD) conditions, respectively. Our process was performed on the MATLAB deep learning platform and tested on CR and CD solar energy conditions. The excellent execution of the suggested technique was compared with state-of-the-art deep neural network solar forecasting techniques. © 2022 by the authors.