A k‐nearest neighbor‐based averaging model for probabilistic PV generation forecasting

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.autoregressive conditional heteroscedastic (ARCH), k-nearest neighbors (kNN), multiple linear regression (MLR), probabilistic PV generation forecasting, quantile regression averaging (QRA) | INTRODUCTIONIn recent days, research and development in PV technology have risen multi-folds making it more efficient, cost-effective, and easy to commission, leading toward a viable alternative to fossil fuels. Integrating PV generations into power systems requires tackling many challenges in planning, operation, and maintenance. 1,2 The variable PV power

Potential Induced Degradation in Photovoltaic Modules: A Review of the Latest Research and Developments

“…K-nearest neighbors (KNNs) classify data points based on the characteristics of their nearest neighbors. It is easy to implement and understand [59][60][61] and can handle highdimensional data. However, it can be computationally intensive to find the K nearest neighbors for each data point [62].…”

Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model

“…Machine learning, as a powerful data processing tool, has been applied to a wide range of fields such as the energy trading market [11] and power grids [12] and also plays a crucial role in wind power forecasting. Conventional machine learning methods include the K-nearest neighbors algorithm (KNN) [13], support vector regression (SVR) [14], decision tree (DT) [15], multilayer perceptron (MLP) [16], etc. Unfortunately, conventional machine learning methods for wind power prediction have difficulty dealing with the increasingly extensive data associated with high-dimensional large-scale wind power generation and suffer from under-fitting and dimensional catastrophes [17].…”