## 1 Introduction
Deeper review of the prior studies on WPP new prediction approaches are still in demand to enhance accuracy and maintain consistency of forecasts while keeping an acceptable computational time. This purpose inspired the implementation of integrated and dual-step WPP models based on various ML techniques. These models use both statistical (wind energy measurement values) and physical (weather information) datasets to implement improved WPPs. Namely, dual-step WPP based on the adaptive neuro-fuzzy inference system (ANFIS) [16], the hybrid particle swarm optimization (PSO)-artificial neural network (ANN) [17], the hybrid genetic algorithm (GA)-ANN [18], and the hybrid Hilbert-Huang transform (HHT)-GA-ANN [19] have been applied for WPP using historical wind energy dataset, wind speed actual observation, and meteorological forecasts. In addition, dual-stage game theory-based ML approaches have been applied for demand response management in the context of a smart grid [20; 21]. These methods have achieved very acceptable prediction accuracy and suggested innovative techniques of treating skipped wind farm power records. However, the prediction accuracies obtained by these methods are not quite enough and can be further improved. The improvement can be achieved by employing more robust and global optimization techniques for training and obtaining the best parameter sets of the forecast engines and by applying more suitable feature extraction algorithms and reconfiguring the forecast engines, as proposed in this paper. Some of the dual-step ML methods are also computationally exhaustive [20; 21] for wind power forecasting problem. In this study, a new, effective, and computationally simple dual-step 24 h-ahead WPP integrated ML model consisting of WT, ant colony optimization algorithm (ACO), and feedforward artificial neural network (FFANN) is devised. The devised model trains with datasets from wind farm actual power generation and numerical weather prediction (NWP). The devised dual-step integrated WT-ACO-FFANN WPP model is compared with persistence, dual-step BP-FFANN, dual-step GA-FFANN, and dual-step PSO-FFANN based WPP models to validate its suitability regarding prediction accuracy and running time performances. The key aims of this study are outlined as follows:  
1. Provide a new and effective dual-step integrated ML model for 24 h-ahead WPP considering wind farm power generation and weather forecast datasets;
2. Improve forecast accuracy, validated through performance comparison with persistence-based reference models and other ML based models;
3. Recommend practical insights for WPP problems involving training datasets with skipped entries. The remaining sections of the paper are outlined as follows. Section 2 provides the devised dual-step WPP model. Section 3 presents the depiction and the preparation of the forecast inputs. The WT-ACO-FFANN configuration for WPP and the theory and mathematical modeling of WT, ACO, and FFANN are described in Section 4. Section 5 defines the various criteria employed for estimating the effectiveness of the devised technique. The experimental outcomes and validations for the investigated operational wind generation system are discussed in Section 6. The study is finally summarized in Section 7.