Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item WAVE FORECASTING FOR THE WEST COAST OF IHDIA(American Society of Civil Engineers (ASCE), 1970) Dattatri, J.; Renukaradhya, P.S.The applicability of the general Wave Forecasting procedures like the SMB and the PNJ methods, to the Indian coasts is studied. The study consisted in analysing the bynoptic charts to obtain the necessary wind characteristics. The computed wind characteristics were used in the above Forecasting methods to yield significant wave heights These were compared with the wave characteristics as recorded by a sub-surface pressure type recorder after suitable modifications to account for the attenuation of wave pressure with depth. The predicted wave heights compare well with the recorded wave heights and the SMB method predicts wave heights better for the case studied. © 1970 American Society of Civil Engineers.Item Weekly prediction of tides using neural networks(Elsevier Ltd, 2015) Salim, A.M.; Dwarakish, G.S.; Liju, K.V.; Thomas, J.; Devi, G.; Rajeesh, R.Knowledge of tide level is essential for explorations, safe navigation of ships in harbour, disposal of sediments and its movements, environmental observations and in many more coastal engineering applications. Artificial Neural Network (ANN) is being widely applied in coastal engineering field for solving various problems. Its ability to learn highly complex interrelationships based on the provided data sets, along with less amount of data requirement, makes it a powerful modelling tool. The present work is related to predicting the hourly tide levels at Mangalore, Karnataka, using a week's hourly tidal levels as input. The data has been obtained from NMPT, Mangalore and is made use of in predicting tide level using Feed Forward Back Propagation (FFBP) and Non-linear Auto Regressive with eXogenous input (NARX) network. FFBP network yielded correlation coefficient value of 0.564 and NARX network yielded very high correlation coefficient of the order 0.915 for predictions of yearlong hourly tide levels. The study proves that ANN technique can be successfully utilized for the prediction of tides at Mangalore. © 2015 Published by Elsevier Ltd.Item Impact of COVID-19 on the Sectors of the Indian Economy and the World(Springer Science and Business Media Deutschland GmbH, 2023) Gite, R.; Vathsala, H.; Koolagudi, S.G.It is known that the SARS-CoV2 (More popularly known as Corona Virus) has affected the way countries function. It has influenced the general health and economy of various countries. Earlier studies have discussed the economic repercussions of various epidemics qualitatively. This paper discusses employing correlation analysis in combination with machine learning techniques to determine the impact of the virus on country’s economic health. The results are justified by the trends seen in pre-COVID, COVID and post-COVID phases there by providing a base for predicting economic conditions of the world in case of any such pandemics in the future. The study includes country-wise analysis for which fifteen country’s economic data is analyzed and sector-wise impact analysis with a specific case of India has been attempted. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item 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.Item 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.Item Ensemble RDLR Architecture for Short-Term Solar Power Forecasting(Institute of Electrical and Electronics Engineers Inc., 2024) Ayappane, H.; Kashyap, Y.Given the drastic shift of global sentiment towards renewable energy, it becomes incredibly important to match supply with demand. However the highly variable nature of weather makes it difficult to accurately predict the output of a solar power plant. Through this paper, we will approach this problem by using an ensemble model consisting of both machine learning and neural networks (NN) as base models to forecast the amount of energy that needs to be produced by a solar plant over a short-term time horizon, which in our case will be 0 minute (immediate), 5 minute, 30 minute and 90 minute. Each base model is fine tuned to encourage high diversity and low correlation to improve prediction accuracy. The expected stability or generalization from RF-DNN combined with the memory retention capability of the LSTM network should provide an ideal predictor for time series forecasting of a stochastic process like weather. © 2024 IEEE.Item AI based Solar Power Forecasting(Institute of Electrical and Electronics Engineers Inc., 2024) Jain, N.; Naik, D.Maintaining equilibrium between generation and load is crucial for maximizing economic scheduling in smart grids. As solar energy forecasting gains importance due to its sporadic nature and climatic dependencies, this study leverages advanced machine learning and deep learning models for accurate prediction. Specifically, we use XGBoost and Long Short-Term Memory (LSTM) models to analyze data from two solar installations in India over a 34-day period. Our approach enhances the efficiency and reliability of solar energy utilization in smart grids. Evaluated over a 3-day test period, the LSTM model achieved an RMSE of approximately 2870 kW, a 22% improvement over a baseline model with an RMSE of 3699 kW. These results highlight the potential of machine learning and deep learning to improve solar power forecasting accuracy, thereby facilitating more effective energy management in smart grids. © 2024 IEEE.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.