Abstract

Electricity generation from burning fossil fuel is one of the major contributors to global warming. Renewable energy sources are a viable alternative to produce electrical energy and to reduce the emission from power industry. They have unlocked opportunities for consumers to produce electricity locally and use it on-site that reduces dependency on centralized generation. Despite the widespread availability, one of the major challenges is to understand their characteristics in a more informative way. Wind energy is highly dependent on the intermittent wind speed profile. This paper proposes the prediction of wind speed that simplifies wind farm planning and feasibility study. Twelve artificial intelligence algorithms were used for wind speed prediction from collected meteorological parameters. The model performances were compared to determine the wind speed prediction accuracy and model comparison for different sizes of data set. The results show, the most effective algorithm varies based on the data size.

 抽象的

燃烧化石燃料发电是全球变暖的主要原因之一。可再生能源是生产电能和减少电力工业排放的可行替代方案。他们为消费者提供了在当地生产电力并就地使用的机会，从而减少了对集中发电的依赖。尽管其可用性广泛，但主要挑战之一是以更丰富的信息方式了解其特征。风能高度依赖于间歇性风速剖面。本文提出了风速预测，简化了风电场规划和可行性研究。使用十二种人工智能算法根据收集的气象参数进行风速预测。对模型性能进行比较，以确定风速预测精度以及不同规模数据集的模型比较。结果表明，最有效的算法根据数据大小而变化。