ABSTRACT

Accurate prediction of future wind speed is important for wind energy integration into the power grid. Wind speeds are usually measured and predicted at lower heights, while modern wind turbines have hub heights of about 80–120 m. As per understanding, this is first attempt to analyze predictability of wind speed with height. To achieve this, wind data was collected using Laser Illuminated Detection and Ranging (LiDAR) system at 10 m, 20 m, 40 m, 90 m, 120 m, 200 m, 250 m and 300 m heights. The collected data is used for training and testing an Artificial Neural Network (ANN) for hourly wind speed prediction for each of the future 12 hours, using 48 previous hourly values. Careful analyses of short term wind speed prediction at different heights and future hours show that wind speed is predicted more accurately at higher heights. For example, the mean absolute percent error decreases from 0.25 to 0.12 corresponding to heights 10 to 300 m, respectively for the 6th future hour prediction, an improvement of around 50%. The performance of ANN method is compared with hybrid genetic algorithm and ANN method namely GANN. Results showed that GANN outperformed ANN for most of the heights for prediction of wind speed at the future 6th hour. Results are also confirmed on another data set and other methods.

摘要

准确预测未来的风速对于将风能集成到电网中至关重要。通常在较低的高度测量和预测风速，而现代风力涡轮机的轮毂高度约为80–120 m。据了解，这是首次尝试分析风速随高度的可预测性。为此，使用激光照明探测与测距（LiDAR）系统在10 m，20 m，40 m，90 m，120 m，200 m，250 m和300 m高度收集风数据。收集到的数据用于训练和测试人工神经网络（ANN），以使用之前的48个小时值对未来12小时中的每个小时进行每小时风速预测。对不同高度和未来小时的短期风速预测进行的仔细分析表明，在较高高度下风速预测更为准确。例如，对于第六个未来小时的预测，平均绝对百分比误差分别从0.25降低到0.12，对应于10到300 m的高度，提高了约50％。将ANN方法的性能与混合遗传算法和ANN方法即GANN进行了比较。结果表明，在未来的第6小时，GANN在大多数高度上均优于ANN。还可以通过其他数据集和其他方法来确认结果。