A k‐nearest neighbor‐based averaging model for probabilistic PV generation forecasting. (4th January 2022)

• Record Type: Journal Article
• Authors: Tripathy, Debesh Shankar; Prusty, B Rajanarayan; Bingi, Kishore
• Is Part Of: International journal of numerical modelling. Volume 35:Number 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-04
• Subjects: autoregressive conditional heteroscedastic (ARCH) -- k‐nearest neighbors (kNN) -- multiple linear regression (MLR) -- probabilistic PV generation forecasting -- quantile regression averaging (QRA); Electric networks -- Mathematical models -- Periodicals; Electronics -- Mathematical models -- Periodicals; 621.3011
• DOI: 10.1002/jnm.2983
• Languages: English
• ISSNs: 0894-3370

Abstract

Abstract: Probabilistic forecasting of PV generation is crucial in uncertainty management to reinforce PV‐integrated power systems for long‐term planning. In this context, developing a reliable probabilistic forecast model is challenging due to weather conditions' stochastic nature and varying daily PV production patterns at multiple time instants. Due to varying probability distribution patterns, a nonparametric approach, such as quantile regression, is challenging to approximate the forecast error distribution. A forecast combination concept that yields impressive probabilistic forecasting results has rarely been utilized for PV generation forecasting. The use of complementary and sensible point forecasters characterizing different aspects of intricate PV production patterns in a k ‐nearest neighbor quantile regression averaging framework is the proposed probabilistic forecasting model. The proposed forecasting model's accuracy is assessed with real‐world PV generation data from the USA for multi‐time instants. The probabilistic forecast performance of the proposed quantile k ‐nearest neighbors regression averaging model is evaluated against the traditional quantile regression averaging, quantile regression forests, quantile k ‐nearest neighbors, basic quantile regression, and regression bootstrapping in terms of the forecast (quantiles and prediction intervals) accuracy, using relevant error measures and scores. The proposed model has superior performance compared to the other models, as indicated by the lowest forecast errors and scores.