Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Chiranjeevi, M.

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    Solar Irradiation Forecast Enhancement Using Hybrid Architecture
    (Institute of Electrical and Electronics Engineers Inc., 2023) Chiranjeevi, M.; Karlamangal, S.; Moger, T.; Jena, D.
    Power balancing at the grid is much more involved process due to the fact that solar power generation is primarily weather dependent, as it is relied on solar irradiation, which is very volatile and unpredictable. Accurate solar irradiation forecasting can significantly increase the performance of solar power plants. This research is motivated by the current advancements in deep learning (DL) models and its practical use in the green energy field. The proposed model combines two DL architectures: convolutional neural network (CNN) and long short-term memory (LSTM). The effectiveness of the same is analysed by comparing with recurrent neural network (RNN) family architectures. The RNN family models are Long Short Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-directional GRU (Bi-GRU). The simulations are conducted on a publicly available data set from Desert Knowledge Australia Solar Centre (DKASC), Australia. A meteorological station across the Northern Territory (NT Solar resource) collects high resolution solar and climate data from Darwin location, which is used for the experiment. From the results, it is evident that each of the bidirectional model outperform its unidirectional equivalent architectures. However, the hybrid network (CNN-LSTM) outperforms all the individual models as per the error metric analysis. © 2023 IEEE.
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    Preprocessing Techniques of Solar Irradiation Data
    (Institute of Electrical and Electronics Engineers Inc., 2023) Chiranjeevi, M.; Karlamangal, S.; Moger, T.; Jena, D.; Agarwal, A.
    Solar energy being abundant, non-exhaustive, environmentally friendly attracts the people attention towards the alternate renewable energy. High-quality time series data is essential for producing an accurate estimate of solar power generation. In most cases, the plethora of information hidden in time series data cannot be accessed. Common issues with time series include outliers, noise, missing data, and a lack of order in the timestamps itself that impair forecasting accuracy. So, preprocessing of the input data is a mandate in order to achieve a precise and dependable forecast. This study proposes various pre-processing techniques to improve the performance of the forecasting accuracy. The different ways to handle the missing values and outliers detection by sliding window method and box plots are presented in this study. The solar irradiation data collected from solar center Alice Springs, Australia used for validation of the preprocessing results. The efficacy of the proposed method in detecting the missing values and outliers is effective from the obtained results. © 2023 IEEE.
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    Robust Solar Irradiance Prediction: A Hybrid Approach Using XGBoost for Feature Extraction and WaveNet for Forecasting
    (Springer Science and Business Media Deutschland GmbH, 2025) Chiranjeevi, M.; Moger, T.; Jena, D.
    Accurately forecasting solar irradiance is crucial for maximizing solar energy utilization. However, in practical applications, the complex nature of irradiance patterns and the common issue of missing data pose significant challenges, making precise predictions difficult and increasing uncertainty and instability in forecasts. This paper addresses the challenge of predicting solar power output, particularly in scenarios where equipment failures lead to inaccurate or missing data. To overcome these issues, effective preprocessing techniques are employed to improve data quality before forecasting. XGBoost is utilized for feature extraction, ensuring that the model identifies and leverages the most relevant features. Additionally, a WaveNet model is used for solar irradiance prediction, capitalizing on its computational efficiency and sensitivity to small fluctuations in the data. This integrated approach aims to enhance the accuracy of solar irradiance predictions, even in the presence of data irregularities. The results suggest that the proposed model outperforms other benchmark models in terms of performance metrics achieving an R2 score of 0.9733. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.