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The accurate training of a wind power forecasting (WPF) model for a newly built wind farm is difficult because of limited historical data. This study established a multitask learning architecture wherein the WPF in different wind farms represents an independent task. Subsequently, a novel short-term WPF model based on a multitask learning architecture was proposed. In this model, a multitask Gaussian process is used to capture the intertask conjunction, which contributed to the training of each task. The proposed methodological framework employs dependencies from other tasks wherein older wind farms contain substantial historical data to enhance the performance of tasks in which there is a newly built wind farm. Several numerical experiments were conducted using datasets from seven independent wind farms in Australia. The results show that the proposed scheme not only obtains improved point forecasting results but also produces better probabilistic forecasting results, thus demonstrating the superiority of the proposed method.

Wind energy projects provide clean energy so that they should be increased to reach the sustainable development goals of the countries. However, current decision-making process should be improved for the effectiveness of these projects. Thus, critical factors should be considered to understand the significant indicators of the performance of the wind energy projects. This article aims to determine the factors that should be considered when deciding on wind energy investments. In this context, 9 different criteria belonging to 3 dimensions (project, firm, market) are determined based on literature review. Later, an analysis is carried out by using hesitant interval-valued intuitionistic fuzzy (IVIF) Decision Making Trial and Evaluation Laboratory (DEMATEL) to identify the most important factors. Furthermore, 4 different investment strategies in Boston Consultancy Group (BCG) matrix have been determined as alternatives. To determine which of these strategies is suitable for wind energy investments, the hesitant IVIF multi-objective optimization on the basis of ratio analysis (MOORA) method has been considered. Additionally, a comparative evaluation is also performed by using technique for order preference by similarity to ideal solution (TOPSIS) methodology. Similarly, sensitivity analysis is also made by considering 9 different cases. The analysis results of different methodologies are quite similar which shows the coherency and reliability of the findings. It is concluded that firm-based factors play the most significant role. It is also identified that technical development, financial performance and organizational effectiveness are the most significant criteria to make investment decision on wind energy projects. Furthermore, due to the market growth potential, it is recommended that wind energy investors increase their investments and strengthen their position in the market.

With the rapid increasing of wind power generation in the power system, the coordinated dispatch of active and reactive power for each wind turbine (WT) in the wind farm (WF) becomes the critical issue for the safe and stable of power grid. Considering the time-varying characteristic of the WF, this can be regarded as a decision-making problem under uncertainty. To this end, this study formulates the active and reactive power dispatch problem of WF as a Markov decision process (MDP) allowing for the system uncertainty, e. g. wind speed, reactive power demand and wake effect. Then, an agent is trained via deep reinforcement learning algorithm (DRL) to solve the MDP to obtain the optimal dispatch policy with the minimizing levelized production cost (LPC) target. Finally, the proposed method is tested on an 80 MW WF and some benchmark methods are utilized to act as comparison examples. Simulation results show that, compared with other methods, the proposed dispatch strategy can provide more appropriate active and reactive reference for each wind turbine to extend lifetime of WF, resulting in less LPC.