Conference Papers

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    Multi-time instant probabilistic PV generation forecasting using quantile regression forests
    (Institute of Electrical and Electronics Engineers Inc., 2020) Tripathy, D.S.; Prusty, B.; Jena, D.; Sahu, M.K.
    Long-term planning for the reinforcement of power systems with PV-integration requires multi-time instant PV generation uncertainty modeling. Probabilistic forecasting of PV generation plays a vital role in the uncertainty management in power systems with PV penetration. An ensemble approach for probabilistic PV generation forecasting, such as the quantile regression forests, proves to be a suitable model because it models the uncertain PV generation more accurately compared to single mean models. The inherent nature of forests to prevent over-fitting by "bagging" the training data is an advantage. Also, the optimal choice of the model hyper-parameters adds to its efficiency as a forecaster. Further, the stochastic nature of weather conditions needs the selection of sensible regressors for the proposed quantile regression forests framework based on the physics of the underlying phenomenon. Real-world data for PV generation collected at multiple instants of time from the USA are employed to test the efficacy of the proposed probabilistic forecasting. The proposed model is compared against the basic quantile regression approach in terms of the accuracy of the quantile forecasts as well as prediction intervals using suitable scores and error metrics. © 2020 IEEE.
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    A Machine Learning Approach for Daily Temperature Prediction Using Big Data
    (Springer Science and Business Media Deutschland GmbH, 2022) Divakarla, U.; Chandrasekaran, K.; Hemant Kumar Reddy, K.H.K.; Reddy, R.V.; Rao, M.
    Due to global warming, weather forecasting becomes complex problem which is affected by a lot of factors like temperature, wind speed, humidity, year, month, day, etc. weather prediction depends on historical data and computational power to analyze. Weather prediction helps us in many ways like in astronomy, agriculture, predicting tsunamis, drought, etc. this helps us to be prepared in advance for any kinds disasters. With rapid development in computational power of high end machines and availability of enormous data weather prediction becomes more and more popular. But handling such huge data becomes an issue for real time prediction. In this paper, we introduced the machine learning-based prediction approach in Hadoop clusters. The extensive use of map-reduce function helps us distribute the big data into different clusters as it is designed to scale up from single servers to thousands of machines, each offering local computation and storage. An ensemble distributed machine learning algorithms are employed to predict the daily temperature. The experimental results of proposed model outperform than the techniques available in literature. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Performance Comparison of Machine Learning Algorithms in Groundwater Potability Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2022) Kuruvilla, E.; Kundapura, S.
    Rising global water demand has resulted in the overuse of groundwater resources and a decline in groundwater quality. Physical and chemical characteristics significantly impacted by geological formations and human activities determine how groundwater quality varies. An accurate and reliable assessment of groundwater resource information is the key element for effective management and enhancement of groundwater quality. The utilization of modern Machine Learning (ML) techniques in groundwater quality assessment provides insights for policymakers in suggesting remedies and management approaches for groundwater quality issues. Machine Learning models outperform other simulation models, using input and output datasets without considering the intricate relationship of the model to be analyzed and decreasing computational efforts. Comparison of various ML techniques, including Ensemble, Nonlinear, and Linear models for the prediction of groundwater potability is the main objective of this study. The presence of potable groundwater suggests that the water is fit for human consumption. The proposed approach makes use of eight ML algorithms i.e. Gradient Boosting Classifier (GB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Linear Regression (LR) and Stochastic Gradient Descent (SGD) algorithm. According to the results, the Ensemble ML models outperformed well followed by the Nonlinear models, and Linear classification ML models have comparatively less accuracy and reliability. © 2022 IEEE.