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

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'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.