Faculty Publications
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Item Solar Distillation and Water Heating Systems Integration with Photovoltaic Technology(Springer, 2024) Dev, R.; Kashyap, Y.; Tewari, K.; Pal, P.Solar energy is a renewable source with three major applications: photovoltaics (PV), thermal, and daylight. A photovoltaic cell has a conversion efficiency of around 16–35%, depending upon its fabrication technology. Hence, it is observed that ~65–84% of incident solar radiation is lost as thermal energy to the surroundings. At the same time, solar thermal has vast applications, e.g., solar water heating, solar greenhouse drying, solar greenhouse crop cultivation, solar distillation, solar aquaculture, etc. Solar thermal applications have a thermal efficiency of around 20–45% depending upon fabrication materials, design, operating, and weather conditions. Integrating photovoltaic and thermal applications proved advantageous over their application with better overall efficiency. Over the years, many researchers have developed various concepts integrating these technologies to get more output, cost, and land use benefits. This chapter elaborates on different ‘PV-integrated solar distillation systems’ and ‘PV-integrated solar water heating systems’ with working principles and performances. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Enhancing High-Frequency PV Power Forecast Using Optimal Hyperparameter Setting in LSTM(Springer Science and Business Media Deutschland GmbH, 2025) Kumar, A.; Kashyap, Y.; Nasar, R.Solar energy plays a significant role in the world’s shift to renewable and sustainable energy. So, accurate forecasting techniques are essential for effective grid management and smooth integration into current energy infrastructures. Traditional solar forecasting approaches often encounter limitations in capturing the complex and nonlinear relationships inherent in solar power generation patterns. In response to these challenges, the present paper demonstrates the forecast analysis of high-frequency (HF) PV power components, which is obtained with the decomposition of actual PV power data. The focus of this paper is on the analysis of high-frequency PV power components as they exhibit high fluctuation. To capture this high fluctuation feature present in PV power, a moving average filter is applied to smooth the input data and potentially enhance the 60 min ahead forecasting performance using the long short-term memory (LSTM) model. The best-performing LSTM model has secured MAE= 1.114 % and RMSE = 2.608 % for 60 min ahead forecast. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Rooftop photovoltaic energy production management in india using earth-observation data and modeling techniques(MDPI AG rasetti@mdpi.com Postfach Basel CH-4005, 2020) Masoom, A.; Kosmopoulos, P.; Kashyap, Y.; Kumar, S.; Bansal, A.This study estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of clouds and aerosols on the PV energy production based on earth observation (EO)-related techniques and solar resource modeling. The post-processed satellite remote sensing observations from the INSAT-3D have been used in combination with Copernicus Atmosphere Monitoring Service (CAMS) 1-day forecasts to perform the Indian Solar Irradiance Operational System (INSIOS) simulations. NITK experiences cloudy conditions for a major part of the year that attenuates the solar irradiance available for PV energy production and the aerosols cause performance issues in the PV installations and maintenance. The proposed methodology employs cloud optical thickness (COT) and aerosol optical depth (AOD) to perform the INSIOS simulations and quantify the impact of clouds and aerosols on solar energy potential, quarter-hourly monitoring, forecasting energy production and financial analysis. The irradiance forecast accuracy was evaluated for 15 min, monthly, and seasonal time horizons, and the correlation was found to be 0.82 with most of the percentage difference within 25% for clear-sky conditions. For cloudy conditions, 27% of cases were found to be within ±50% difference of the percentage difference between the INSIOS and silicon irradiance sensor (SIS) irradiance and it was 60% for clear-sky conditions. The proposed methodology is operationally ready and is able to support the rooftop PV energy production management by providing solar irradiance simulations and realistic energy production estimations. © 2020 by the authors.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.Item Solar Photovoltaic Hotspot Inspection Using Unmanned Aerial Vehicle Thermal Images at a Solar Field in South India(MDPI, 2023) Umesh, P.; Kashyap, Y.; Baxevanaki, E.; Kosmopoulos, P.The sun is an abundant source of energy, and solar energy has been at the forefront of the renewable energy sector for years. A way to convert it into electricity is by the use of solar cells. Multiple solar cells, connected to each other, create solar panels, which in their turn, are connected in a solar string, and they create solar farms. These structures are extremely efficient in electricity production, but also, cells are fragile in nature and delicate to environmental conditions, which is the reason why some of them show discrepancies and are called defective. In this research, a thermal camera mounted on a drone has been used for the first time in the solar farm operating conditions of India in order to capture images of the solar field and investigate solar panels for defective cells and create an orthomosaic image of the entire area. This procedure next year will be established on an international scale as a best practice example for commercialization, providing effortless photovoltaic monitoring and maintenance planning. For this process, an open source software WebODM has been used, and the entire field was digitized so as to identify the location of defective panels in the field. This software was the base in order to provide and analyze a digital twin of the studied area and the included photovoltaic panels. The defects on solar cells were identified with the use of thermal bands, which record and point out their temperature of them, whereas anomalies in the detected temperature in defective solar cells were captured using thermal electromagnetic waves, and these areas are mentioned as hotspots. In this research, a total number of 232.934 solar panels were identified, and 2481 defective solar panels were automatically indicated. The majority of the defects were due to manufacturing failure and normal aging, but also due to persistent shadowing and soiling from aerosols and especially dust transport, as well as from extreme weather conditions, including hail. The originality of this study relies on the application of the proposed under development technology to the specific conditions of India, including high photovoltaic panels wear rates due to extreme aerosol loads (India presents one of the highest aerosol levels worldwide) and the monsoon effects. The ability to autonomously monitor solar farms in such conditions has a strong energy and economic benefit for production management and for long-term optimization purposes. © 2023 by the authors.Item Algorithms for Hyperparameter Tuning of LSTMs for Time Series Forecasting(MDPI, 2023) Dhake, H.; Kashyap, Y.; Kosmopoulos, P.The rapid growth in the use of Solar Energy for sustaining energy demand around the world requires accurate forecasts of Solar Irradiance to estimate the contribution of solar power to the power grid. Accurate forecasts for higher time horizons help to balance the power grid effectively and efficiently. Traditional forecasting techniques rely on physical weather parameters and complex mathematical models. However, these techniques are time-consuming and produce accurate results only for short forecast horizons. Deep Learning Techniques like Long Short Term Memory (LSTM) networks are employed to learn and predict complex varying time series data. However, LSTM networks are susceptible to poor performance due to improper configuration of hyperparameters. This work introduces two new algorithms for hyperparameter tuning of LSTM networks and a Fast Fourier Transform (FFT) based data decomposition technique. This work also proposes an optimised workflow for training LSTM networks based on the above techniques. The results show a significant fitness increase from 81.20% to 95.23% and a 53.42% reduction in RMSE for 90 min ahead forecast after using the optimised training workflow. The results were compared to several other techniques for forecasting solar energy for multiple forecast horizons. © 2023 by the authors.Item Rooftop Photovoltaic Energy Production Estimations in India Using Remotely Sensed Data and Methods(MDPI, 2023) Kumar, A.; Kosmopoulos, P.; Kashyap, Y.; Gautam, R.We investigate the possibility of estimating global horizontal irradiance (GHI) in parallel to photovoltaic (PV) power production in India using a radiative transfer model (RTM) called libRadtran fed with satellite information on the cloud and aerosol conditions. For the assessment of PV energy production, we exploited one year’s (January–December 2018) ground-based real-time measurements of solar irradiation GHI via silicon irradiance sensors (Si sensor), along with cloud optical thickness (COT). The data used in this method was taken from two different sources, which are EUMETSAT’s Meteosat Second Generation (MSG) and aerosol optical depth (AOD) from Copernicus Atmospheric Monitoring Services (CAMS). The COT and AOD are used as the main input parameters to the RTM along with other ones (such as solar zenith angle, Ångström exponent, single scattering albedo, etc.) in order to simulate the GHI under all sky, clear (no clouds), and clear-clean (no clouds and no aerosols) conditions. This enabled us to quantify the cloud modification factor (CMF) and aerosol modification factor (AMF), respectively. Subsequently, the whole simulation is compared with the actual recorded data at four solar power plants, i.e., Kazaria Thanagazi, Kazaria Ceramics, Chopanki, and Bhiwadi in the Alwar district of Rajasthan state, India. The maximum monthly average attenuation due to the clouds and aerosols are 24.4% and 11.3%, respectively. The energy and economic impact of clouds and aerosols are presented in terms of energy loss (EL) and financial loss (FL). We found that the maximum EL in the year 2018 due to clouds and aerosols were 458 kWh m−2 and 230 kWh m−2, respectively, observed at Thanagazi location. The results of this study highlight the capabilities of Earth observations (EO), in terms not only of accuracy but also resolution, in precise quantification of atmospheric effect parameters. Simulations of PV energy production using EO data and techniques are therefore useful for real-time estimates of PV energy outputs and can improve energy management and production inspection. Success in such important venture, energy management, and production inspections will become much easier and more effective. © 2023 by the authors.Item Multi-Layer Cloud Motion Vector Forecasting for Solar Energy Applications(Elsevier Ltd, 2024) Kosmopoulos, P.; Dhake, H.; Melita, N.; Tagarakis, K.; Georgakis, A.; Stefas, A.; Vaggelis, O.; Korre, V.; Kashyap, Y.Real-time forecasting of solar radiation posses several benefits and has huge potential for industrial applications. However, the intermittent nature of solar radiation makes it difficult to forecast accurately. Cloud cover is one of the major influencing factors of solar radiation. Thus, forecasting cloud motion effectively can help to improve the accuracy of short-term solar radiation forecasts. In this study, a novel Multi-Layer Cloud Motion Vector (referred as 3D-CMV) forecasting technique was introduced, which combined with the fast radiative transfer model (FRTM) produces forecasts up to 3 h ahead at 15 min intervals over 5km × 5km grids across Europe and North Africa. The cloud microphysics obtained from the Support to Nowcasting and Very Short Range Forecasting (SAFNWC) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) was used as input to the forecasting system. The results obtained improvements in forecasts as compared to the conventional cloud motion vector techniques, across all seasons and sky conditions. Comparisons against ground-based measurements from the Baseline Surface Radiation Network (BSRN) revealed an overall maximum percentage difference of less than 12%, bias under -20 Wm−2 and a root mean square error (RMSE) under 80 Wm−2. Performance evaluations of Multi-Layer Cloud Motion Vector has been performed against several state-of-the-art techniques and presented in this study. Short-term solar energy forecasting has an established market and rising demand. Accurate forecasts from Multi-Layer CMV hence pose a high potential for real world applications. © 2023 The AuthorsItem Ray-Tracing modeling for urban photovoltaic energy planning and management(Elsevier Ltd, 2024) Kosmopoulos, P.; Dhake, H.; Kartoudi, D.; Tsavalos, A.; Koutsantoni, P.; Katranitsas, A.; Lavdakis, N.; Mengou, E.; Kashyap, Y.The traditional Radiative Transfer Modelling solutions for Solar Energy monitoring and forecasting often provide outputs for a single point location or an area location. However, for high resolution representation of areas these solutions suffer due to low simulation speeds. This approach makes it difficult for decision-makers to accurately estimate the solar potential of the administrative area and plan installations accordingly. In this direction, the study introduces three-dimensional Ray-Tracing based radiative modeling which is a high-speed area-based solution for solar energy monitoring. The three-dimensional ray-tracing was simulated by using advanced graphic creation platforms and cloud computing in conjunction with satellite data of the clouds, aerosols, building shadows effects and three-dimensional representations of the city using Cesium 3D tiles and Unreal Engine ®. The entire system was developed in a hybrid model to be exploited by urban planners for solar PV installations and by electricity distribution system operators for energy management and efficient incorporation of the produced energy into the regular and smart grids. This study implements and analyses this Ray-Tracing model for solar photovoltaic energy potential estimation at a rooftop level for the city of Athens, Greece. The total rooftop exploitable area in Athens was found to be close to 34 km2, which is able to massively host distributed PVs followed by almost 4.3 TWh of annually produced energy, whilst Penteli (a Municipality in Athens) possessed a potential of 96.8 GWh with an exploitable area of just 0.8 km2. This amount of energy, in a hypothetical full coverage scenario, is able to provide for 48.7% of Athens's total energy requirement. Similar year-long simulations were conducted using the EU's largest rooftop solar installation at Stavros Niarchos Foundation Cultural Center and randomly selected rooftops having solar installations in different municipalities in Athens. With these estimated solar potential values, the gross savings in natural gas consumption and hence the CO2 equivalent emissions can be computed. With the current estimated solar potential of Athens, the analyzed savings accounted of nearly 2.43 billion euros and 18 MT CO2 equivalent emissions. These computed annual savings are capable of covering installation costs for nearly 100,000 new solar installations. The end-product of this study is the development of a solar cadastre web tool which will support the decision-makers in the energy transition policies and the solar PV penetration into the urban environment and eventually drive the effort into renewable energy transition across the globe. © 2024 The Author(s)Item MSSEAG-UNet: A Novel Deep Learning Architecture for Cloud Segmentation in Fisheye Sky Images and Solar Energy Forecast(Institute of Electrical and Electronics Engineers Inc., 2025) Kumar, A.; Kashyap, Y.; Sharma, K.; Vittal, K.; Shubhanga, K.N.This study analyzes sky images captured using a ground-based fisheye camera, aiming to address the challenge of accurately segmenting clouds, which is difficult due to their fuzzy and indistinct boundaries and uneven lighting conditions. Accurate segmentation of clouds in ground-based sky images is crucial for accurate solar energy forecasting. Motivated by these challenges, this article has proposed a novel deep learning architecture called multispatial squeeze-and-excite attention gated U-Net (MSSEAG-UNet) for cloud segmentation in ground-based fisheye sky images. The proposed architecture integrates a multispatial convolutional (MS-CNN) block and squeeze-and-excitation (SE) blocks in the encoder path to improve multiscale feature extraction (MFF) and recalibrate feature maps, while an attention block is incorporated in the decoder path to emphasize key cloud features. The segmentation performance of the MSSEAG-UNet is compared with five benchmark models, and results show that the proposed model outperforms than all benchmarks models. Furthermore, the segmented cloud images produced by the MSSEAG-UNet are used to calculate the cloud percentage, which is then integrated with the original sky images using a multicolumn convolutional model for global horizontal irradiance (GHI) forecast. GHI forecast is conducted for 15-, 30-, and 60-min ahead timesteps, with the best results achieved for the 60-min forecast, yielding mean absolute error (MAE), mean square error (mse), and RMSE values of 6.245%, 0.683%, and 8.265%, respectively. These results highlight the effectiveness of the proposed approach in improving both cloud segmentation accuracy and short-term solar irradiance forecasting. © 1980-2012 IEEE.