College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China.
Comput Intell Neurosci. 2021 Oct 26;2021:1502932. doi: 10.1155/2021/1502932. eCollection 2021.
Accurate electricity load forecasting is an important prerequisite for stable electricity system operation. In this paper, it is found that daily and weekly variations are prominent by the power spectrum analysis of the historical loads collected hourly in Tai'an, Shandong Province, China. In addition, the influence of the extraneous variables is also very obvious. For example, the load dropped significantly for a long period of time during the Chinese Lunar Spring Festival. Therefore, an artificial neural network model is constructed with six periodic and three nonperiodic factors. The load from January 2016 to August 2018 was divided into two parts in the ratio of 9 : 1 as the training set and the test set, respectively. The experimental results indicate that the daily prediction model with selected factors can achieve higher forecasting accuracy.
准确的电力负荷预测是电力系统稳定运行的重要前提。通过对中国山东省泰安市每小时采集的历史负荷进行功率谱分析，发现存在明显的日变化和周变化。此外，外生变量的影响也非常明显。例如，在中国农历春节期间，负荷会持续较长时间显著下降。因此，构建了一个包含六个周期和三个非周期因素的人工神经网络模型。将 2016 年 1 月至 2018 年 8 月的负荷按 9∶1 的比例分为训练集和测试集。实验结果表明，选择因素的日预测模型可以达到更高的预测精度。
