Conference Papers

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

Browse

Subject: search.filters.subject.Multiple linear regression (MLR)

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Short-term PV generation forecasting using quantile regression averaging
    (Institute of Electrical and Electronics Engineers Inc., 2020) Tripathy, D.S.; Prusty, B.R.; Jena, D.
    The globally increasing demand for energy to carry out the various day-to-day activities needs renewable sources in conjunction with existing power plants. PV technology has seen tremendous growth over the past decades. However, the integration of PV generation to the power systems invites numerous planning and operational challenges. In the short-term, the real-time operation of PV-integrated power systems requires the characterization of the uncertainties associated with the PV generation. A probabilistic framework, such as the quantile regression averaging (QRA), has been successful in forecasting load power and electricity spot prices. This paper applies QRA to accomplish a probabilistic forecast of PV generation using its historical record from a rooftop installation at Lincoln, USA. This paper's main contribution is the use of two appropriate individual point forecasters, i.e., autoregressive conditional heteroscedastic and multiple linear regression models, to complement each other and make accurate quantile forecasts. The proposed model is used in the short-term forecasting of PV generation for the four major seasons up to two weeks ahead. A detailed result analysis shows that the combination of both models improves overall forecasting performance rather than using any of the models alone. © 2020 IEEE.
  • No Thumbnail Available
    Item
    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.