# Assessment of Offshore Wind Resources, Based on Improved Particle Swarm Optimization
## 1 Introduction  
Due to environmental issues and the need for energy supply security, renewable energy has been listed in the world's agenda [1]. Notably, wind energy is one of the most cost-effective renewable energy sources in areas with abundant wind resources, and it has been widely used worldwide [2]. [PERSON] et al. [3] analyzed the wind energy resource utilization in the context of the coordinated development in the Beijing-Tianjin-Hebei region of China. It should be stressed that the problem of wind curtailment is serious in China as it not only restricts the development of renewable energy, but also brings economic losses to enterprises and society. [PERSON] et al. [4] studied the wind energy resources of Phuquoc Island in Vietnam, and proved that wind energy can be used as an energy source for about 300,000 rural non-electric households. So far, China has the largest wind power generation capacity worldwide, followed by the United States. Yet, China's utilization of this installed capacity trails that of the U.S by a significant gap [5]. [PERSON] et al. [6] predicted that wind energy will generate 20% of the electricity in the U.S by 2030. However, the contribution of wind power to cover the electricity demand is less certain than conventional power sources. Therefore, the capacity value of wind power is smaller than that of conventional plants. With the global carbon reduction consensus reached and China's '3060' double carbon target proposed, offshore wind power has become the face of the technology frontier and is one of the important development directions in the field of renewable energy [7]. Wind energy is an environment-friendly clean energy, and in the feasibility study stage of theconstruction of offshore wind farms, a reliable wind resource assessment of the proposed area is required [8]. Due to the randomness, volatility and low energy density of wind speed, which leads to the low reliability of the power system operation, the accuracy of wind speed prediction is critical. Accurate wind speed prediction ensures the safe operation of wind farms and the flexible dispatch of power grids connected with new energy sources. Meanwhile, it is also crucial when performing maintenance scheduling and evaluation of the feasibility of building a wind farm in a particular area. There are two main methods for evaluating wind resources at present: an evaluation, based on the observation of the historical data from weather stations or wind towers, and a numerical simulation of the wind energy resource evaluation [9]. Nevertheless, these methods have strong limitations, complexities and a low accuracy. Due to its low resolution, the first method can only reflect the distribution of wind energy resources in a region in a macroscopic manner, and cannot accurately quantify the wind energy reserves. For the accurate wind energy resource assessment, numerical simulation techniques still have to be applied. A probability distribution model, among which the Weibull distribution model is the most suitable method for wind energy resources evaluation, is used to evaluate the wind energy resources. [PERSON] et al. [10] proposed to use a two-parameter Weibull model to evaluate the wind resources in the Bohai Sea of China, based on the Nakagami and Rician distributions. Compared with the two-parameter Weibull distribution, the three-parameter one has better fitting with good results in the no-wind zone or at the low wind speed [11]. However, when there are more parameters, and when the operation speed is slower, the timeliness cannot meet the requirements [12]. To improve the fitness and the accuracy of the probability distribution function, relevant researchers employed intelligent algorithms to evaluate the wind resources. A forecasting method for wind and solar power generation, based on a machine learning algorithm, was proposed [13]. [PERSON] et al. [14] modified the extreme learning machine, but in terms of two, three and 4 h ahead, the wind power forecast performances were quite different. Meanwhile, [PERSON] et al.