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Wind power generation is characterized by high intermittency and volatility owing to the stochastic nature of wind. In addition to forecasting accuracy, forecasting uncertainty quantification can have a major impact on power system energy management and operations planning. In this paper, a new fully co-optimized hybrid short-term probabilistic Wind Power Forecasting (WPF) model is proposed for the construction of Prediction Intervals (PIs) in a spatiotemporal framework. A Multi-Objective Improved Adaptive Particle Swarm Optimization (MOIAPSO) algorithm is developed to optimize the model&#x2019;s parameters. PIs are generated by nonlinear autoregressive networks with exogenous inputs (NARX) using the Lower Upper Bound Estimation (LUBE) method. Unlike previous related work, the components of the proposed hybrid NARX-LUBE-MOIAPSO model, as well as the initial settings and the configuration of the parameters, are determined based on a full co-optimization approach. The co-optimization is performed from a forecasting quality and training time trade-off perspective. Furthermore, a spatiotemporal framework is introduced to improve forecasting performance and comprehension of regional spatiotemporal uncertainty dynamics. The spatiotemporal framework comprises a novel conditional spatiotemporal forecasting methodology and the modeling of spatiotemporal dependencies based on a binary Probabilistic Forecasting Error (PFE) metric. The proposed model and spatiotemporal framework are tested on publicly available datasets consisting of turbine-specific measurements and generate accurate forecasts with efficient uncertainty quantification, while maintaining computational complexity at relatively low levels compared to other state-of-the-art hybrid probabilistic WPF models.

A New Co-Optimized Hybrid Model Based on Multi-Objective Optimization for Probabilistic Wind Power Forecasting in a Spatiotemporal Framework