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.

摘要

燃烧化石燃料发电是全球变暖的主要因素之一。可再生能源是生产电能和减少电力行业排放的可行替代方案。他们为消费者释放了在当地生产电力并在现场使用电力的机会，从而减少了对集中发电的依赖。尽管可用性广泛，但主要挑战之一是以更丰富的方式了解其特征。风能高度依赖于间歇风速曲线。本文提出了对风速的预测，以简化风电场规划和可行性研究。十二种人工智能算法用于根据收集的气象参数预测风速。比较模型性能以确定风速预测准确性和不同大小的数据集的模型比较。结果表明，最有效的算法根据数据大小而变化。