【作者】 徐哲；

【导师】 黄曼磊；

【作者基本信息】 哈尔滨工程大学 ， 电气工程， 2021， 硕士

【摘要】 电力负荷预测是制定电源计划以及保证电网供求之间平衡的基础,提高电力负荷预测的准确性有助于提高电力系统的运行质量和经济效益。想获得高精度的负荷预测结果,充足且高质量的历史数据是一个必要前提。但是由于SACDA设备故障、信号传输受扰、天气影响和人员误操作等原因都会导致电力负荷数据的局部丢失或错误数据的融入,进而影响数据的质量。气象等外在因素的融入为负荷预测的精度提供了方向,深度学习技术的发展为这些外部信息引入神经网络实施有效的智能化预测提供了技术可能和技术手段。本文探讨了多种电力负荷数据缺失问题(尤其是缺失率较大的情况)的应对和解决方式,并利用多种深度学习算法进行了系统负荷预测的研究工作。因使用的负荷数据最小时间尺度为1h,因此仅对建立的预测模型进行了未来1小时和未来一天两种情况下的预测效果验证。本文首先进行了原始历史数据的缺失处理研究。对历史电力负荷数据设置不同程度的缺失率,同时采用了基于统计值、机器学习与深度学习等几类方法进行数据的填充重建,并通过实验对比了不同方法的效果。同时采用重建后的数据对未来负荷进行预测,进一步验证对于缺失数据填充的准确性与填充后数据的质量。然后,进行了多种深度神经网络的应用研究。对比研究了基于多层感知机神经网络、循环神经网络和长短期记忆神经网络的三种预测模型在小时级负荷预测的预测质量。还采用经验模态分解方法将原始负荷序列分解为多个子序列,并分别与长短期记忆网络和随机森林结合,以探究加快模型学习并取得较高的预测精度的方式,并进行了实验验证。最后,引入了温度、湿度、风速等外在信息,利用日期、气象、历史负荷数据构建的多源数据,采用Xgboost算法进行最佳输入特征选择。在此基础上对比了单一模型与混合模型在短期负荷预测的精度。更多还原

【Abstract】 Power load forecasting is the basis for formulating power supply plans and ensuring the balance between power supply and demand.Improving the accuracy of power load forecasting will help improve the operation quality and economic benefits of the power system.To obtain high-precision load forecasting results,sufficient and high-quality historical data is a necessary prerequisite.However,due to SACDA equipment failures,signal transmission disturbances,weather influences and personnel misoperations,etc.,the partial loss of power load data or the integration of erroneous data will affect the quality of the data.The integration of external factors such as weather provides a direction for the accuracy of load forecasting.The development of deep learning technology provides technical possibilities and technical means for the introduction of such external information into neural networks to implement effective intelligent forecasting.This paper discusses the ways to cope with and solve the problem of a variety of power load data missing(especially when the missing rate is large),and uses a variety of deep learning algorithms to carry out the research work of system load forecasting.Because the minimum time scale of the load data used is 1h,only the forecasting effect of the established forecasting model is verified for the next hour and the next day.This article first conducts the research on the missing processing of original historical data.Different degrees of missing rates are set for historical power load data,and several methods based on statistical values,machine learning and deep learning are used to fill and reconstruct the data,and the effects of different methods are compared through experiments.At the same time,the reconstructed data is used to predict the future load to further verify the accuracy of filling the missing data and the quality of the filled data.Then,conducted a variety of deep neural network application research.The forecast quality of hourly load forecasting of three forecasting models based on multilayer perceptron neural network,cyclic neural network and long-and short-term memory neural network is compared and studied.The empirical mode decomposition method is also used to decompose the original load sequence into multiple sub-sequences,which are combined with the long and short-term memory network and random forest respectively to explore ways to speed up model learning and obtain higher prediction accuracy,and conduct experimental verification.Finally,external information such as temperature,humidity,and wind speed is introduced,and multi-source data constructed using date,weather,and historical load data are used to select the best input feature using the Xgboost algorithm.On this basis,the accuracy of single model and hybrid model in short-term load forecasting is compared.更多还原