Accurate electricity load forecast plays an important role in the operation of a power system. Many factors influence the electricity load data such as air temperature, humidity and holidays, and they are taken as the explanatory variables in load forecasting cases traditionally. Search engine indices, a variable may be related to load data which has been never considered before in load forecasting, is discussed and utilized to increase the accuracy of power load prediction in this paper. Spearman's correlation coefficients and Granger test results verify the correlation between Google Trends (GT) and electricity load data. A methodology for processing GT time series with Hodrick-Prescott filter is proposed. To forecast electricity load with an adaptive network model in such a novel situation, we propose a long short-term memory neural network model based on quantum particle swarm algorithm. The performance of load forecast for Long Island region taking GT and weather data as input variables is compared with that taking only weather data as input variables, which shows that the introduction of GT improves short-term forecasting effectiveness significantly.

准确的电力负荷预测在电力系统的运行中起着重要作用。影响用电负荷数据的因素很多，如气温、湿度、节假日等，传统上将其作为负荷预测的解释变量。搜索引擎指标是一个变量，它可能与以前在负荷预测中从未考虑过的负荷数据有关，本文讨论并利用它来提高电力负荷预测的准确性。Spearman 的相关系数和 Granger 检验结果验证了 Google Trends (GT) 与电力负荷数据之间的相关性。提出了一种使用 Hodrick-Prescott 滤波器处理 GT 时间序列的方法。为了在这种新情况下使用自适应网络模型预测电力负荷，我们提出了一种基于量子粒子群算法的长短期记忆神经网络模型。将GT和天气数据作为输入变量的长岛地区负荷预测性能与仅将天气数据作为输入变量进行对比，表明GT的引入显着提高了短期预测的有效性。