Conference Papers

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

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    Comparison of two data cleaning methods as applied to volatile time-series
    (Institute of Electrical and Electronics Engineers Inc., 2019) Ranjan, K.G.; Prusty, B.R.; Jena, D.
    Out-of-sample forecasting of historically observed time-series inevitably necessitates the application of a suitable data cleaning method to assist improved accuracy of the obtained results. The existing data cleaning methods though work amply with nonvolatile time series; fail when applied to a volatile time-series. In this paper, the suitability of the k-nearest neighbor approach and sliding window prediction approach is tested on a set of nonvolatile and volatile time-series. The performance comparison is carried out considering the historical record of furniture sales data, PV generation, load power, and ambient temperature data of different time-steps collected from various places in the USA. Further, the effect of parameters allied with both the methods on the preprocessing result is also analyzed. Finally, possible reforms are suggested for the appropriate preprocessing of volatile time-series. © 2019 IEEE.
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    Uncertainty Management in Utility Grid Considering PV Generation: An Operational Planner's Perspective
    (Institute of Electrical and Electronics Engineers Inc., 2023) Prusty, B.; Bingi, K.; Jena, D.; Badi, M.; Mohan Krishna, S.
    This paper discusses the importance of adopting a spatiotemporal model-based scenario generation technique by approximating a multivariate continuous stochastic process in a discrete form. The computational steps for such a model development are comprehensibly discussed via a case study using PV generation data collected from the USA. The model accuracy comparison through a detailed discussion of obtained results is likely to help novice researchers to choose the best model amongst the available options and tactically decide to improvise the modeling framework further. This systematically prepared paper is expected to help power engineers with enough confidence to execute operational planning decisions realistically. © 2023 IEEE.