Wind power generation output is highly uncertain, since it entirely depends on intermittent environmental factors. This has brought a serious problem to the power industry regarding the management of power grids containing a significant penetration of wind power. Therefore, a highly accurate wind power forecast is very useful for operating these power grids effectively and sustainably. In this study, a new dual-step integrated machine learning (ML) model based on the hybridization of wavelet transform (WT), ant colony optimization algorithm (ACO), and feedforward artificial neural network (FFANN) is devised for a 24 h-ahead wind energy generation forecast. The devised model consists of dual steps. The first step uses environmental factors (weather variables) to estimate wind speed at the installation point of the wind generation system. The second step fits the wind farm actual generation with the actual wind speed observation at the location of the farm. The predicted future speed in the first step is later given to the second step to estimate the future generation of the farm. The devised method achieves significantly acceptable and promising forecast accuracy. The forecast accuracy of the devised method is evaluated through several criteria and compared with other ML based models and persistence based reference models. The daily mean absolute percentage error (MAPE), the normalized mean absolute error (NMAE), and the forecast skill (FS) values achieved by the devised method are 4.67%, 0.82%, and 56.22%, respectively. The devised model outperforms all the evaluated models with respect to various performance criteria.

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Improved-performance day-ahead electricity demand forecast is important to deliver necessary information for right decision of energy management of microgrids. It supports microgrid operators and stakeholders to have better decisions on microgrid flexibility, stability and control. The available conventional forecasting methods for electricity demand at national or regional level are not effective for electricity demand forecasting in microgrids. This is due to the fact that the electricity consumption in microgrids is many times less than the regional or national demands and it is highly volatile. In this paper, an integrated Artificial Intelligence (AI) based approach consisting of Wavelet Transform (WT), Simulated Annealing (SA) and Feedforward Artificial Neural Network (FFANN) is devised for day-ahead prediction of electric power consumption in microgrids. The FFANN is the basic forecasting engine of the proposed model. The WT is utilized to extract relevant features of the target variable (electric load data series) to obtain a cluster of enhanced-feature subseries. The extracted subseries of the past values of the electric load demand data are employed as the target variables to model the FFANN. The SA optimization technique is employed to obtain the optimal values of the FFANN weight parameters during the training process. Historical information of actual electricity consumption, meteorological variables, daily variations, weekly variations, and working/non-working day indicators have been employed to develop the forecasting tool of the devised integrated AI based approach. The approach is validated using electricity demand data of an operational microgrid in Beijing, China. The prediction results are presented for future testing days with one-hour time interval. The validation results demonstrated that the devised approach is capable to forecast the microgrid electricity demand with acceptably small error and reasonably short computation time. Moreover, the prediction performance of the devised approach has been evaluated relative to other four approaches and resulted in better prediction accuracy.

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