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Author: search.filters.author.Kashyap, Y.Subject: search.filters.subject.Hyperparameter

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    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.
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    An Improved ResNet-50 Neural Network Design for PV Panel Image Classification
    (Springer Science and Business Media Deutschland GmbH, 2025) Kumar, A.; Kashyap, Y.; Sharma, A.
    The increasing popularity of photovoltaic (PV) setups stems from their capacity to generate clean and cost-effective electricity. However, various factors can either totally or partially disrupt the production of PV panels. To address this challenge, this study proposes a ResNet-50 model with dynamically adjusted hyperparameters to classify real-time captured images of PV panels into efficient and non-efficient categories. The hyperparameter tuning within the ResNet-50 model is conducted across three distinct cases, revealing that the most optimal classification results are achieved with the following settings: 50 epochs, a learning rate of 0.001, and a batch size of 32. The highest weighted average metrics, including accuracy (96%), recall (96%), precision (97%), and F1-score (96%), were obtained under these settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.