Dynamic Resource Allocation in LTE Radio Access Network Using Machine Learning Techniques

Current LTE networks are experiencing significant growth in the number of users worldwide. The use of data services for online browsing, e-learning, online meetings and initiatives such as smart cities means that subscribers stay connected for long periods, thereby saturating a number of signalling resources. One of such resources is the Radio Resource Connected (RRC) parameter, which is allocated to eNodeBs with the aim of limiting the number of connected simultaneously in the network. The fixed allocation of this parameter means that, depending on the traffic at different times of the day and the geographical position, some eNodeBs are saturated with RRC resources (overused) while others have unused RRC resources. However, as these resources are limited, there is the problem of their underutilization (non-optimal utilization of resources at the eNodeB level) due to static allocation (manual configuration of resources). The objective of this paper is to design an efficient machine learning model that will take as input some key performance indices (KPIs) like traffic data, RRC, simultaneous users, etc., for each eNodeB per hour and per day and accurately predict the number of needed RRC resources that will be dynamically allocated to them in order to avoid traffic and financial losses to the mobile network operator. To reach this target, three machine learning algorithms have been studied namely: linear regression, convolutional neural networks and long short-term memory (LSTM) to train three models and evaluate them. The model trained with the LSTM algorithm gave the best performance with 97% accuracy and was therefore implemented in the proposed solution for RRC resource allocation. An interconnection architecture is also proposed to embed the proposed solution into the Operation and maintenance network of a mobile network operator. In this way, the proposed solution can contribute to developing and expanding the concept of Self Organizing Network (SON) used in 4G and 5G networks.

基于主成分分析和长短期记忆神经网络的光伏功率区间预测

针对光伏发电功率的随机变化,提出一种基于主成分分析和长短期记忆神经网络的光伏功率区间预测方法,有效实现了光伏功率的区间预测。首先,将用于训练模型的输入数据进行主成分分析法降维,在提取数据特征的同时降低数据维度;然后,将降维后的数据与真实光伏功率一同输入基于分位数的长短期记忆神经网络预测模型中迭代训练,得到训练完毕的预测模型;最后,在对比仿真中验证了所提方法的有效性。

基于VMD-LSTMQR的滚动母线负荷区间预测

负荷区间预测能够对负荷出力变化进行概率化分析,准确量化不确定性因素对负荷的影响。相较于传统的点预测,区间预测更能直观反映负荷变化趋势,有助于保障电力系统的安全稳定运行。基于此,文中提出一种基于变分模态分解-长短期记忆神经网络分位数回归(VMD-LSTMQR)的滚动母线负荷区间预测方法。首先,文中采用VMD将原始母线负荷分解成一系列不同频率特征的子序列;接着,确定不同子序列的最优滚动步长并采用LSTMQR分别对不同子序列进行区间预测;最后,将不同子序列的区间预测进行重构,得到原始母线负荷预测结果。文中利用220 kV和10 kV母线负荷数据验证了所采用的区间预测模型相较于传统区间预测模型在预测精度、区间宽度方面得到明显改善。

Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model

Predicting the direction of the stock market has always been a huge challenge.Also,the way of forecasting the stock market reduces the risk in the financial market,thus ensuring that brokers can make normal returns.Despite the complexities of the stock market,the challenge has been increasingly addressed by experts in a variety of disciplines,including economics,statistics,and computer science.The introduction of machine learning,in-depth understanding of the prospects of the financial market,thus doing many experiments to predict the future so that the stock price trend has different degrees of success.In this paper,we propose a method to predict stocks from different industries and markets,as well as trend prediction using traditional machine learning algorithms such as linear regression,polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks:long and short time memory(LSTM)and spoken short-term memory.

基于CNN-BiLSTM的锂电池剩余使用寿命概率密度预测

通过预测锂离子电池的剩余使用寿命(RUL),可以对电池实现管理和维护,提升电池的耐用性和安全性。由于锂离子电池在使用过程中,不同的工况条件会增加锂离子电池RUL预测的不确定性,传统的点预测不能对电池的不确定性进行表达,因此提出了一种基于卷积神经网络(CNN)和双向长短时记忆循环神经网络(BiLSTM)的混合神经网络分位数回归的概率密度预测。该方法通过预测不同分位数条件下的电池容量,不仅可以利用中位数和众数对剩余寿命进行点估计,还可以利用核密度估计得到每个循环周期下电池的容量和剩余使用寿命的概率密度分布,为使用者提供更多有效的决策信息。

基于相似日聚类和QR-CNN-BiLSTM模型的光伏功率短期区间概率预测

精确的短期光伏功率区间概率预测可以有效量化光伏功率预测的不确定性,对于新型电力系统运行调度避险至关重要。为了提高模型预测性能,基于气象变量的数据特征提出模糊C均值(fuzzy C-means,FCM)聚类方法,将历史数据集聚类为晴天、晴转多云和阴雨天,采用与测试集具有相似天气类型的历史数据作为训练样本训练模型;集合卷积神经网络(convolutional neural network,CNN)模型出色的特征提取优势,双向长短期记忆(bidirectional long short term memory,BiLSTM)神经网络模型擅长双向捕捉长时间序列中长期依赖关系的优势,以及可生成区间预测结果的分位数回归(quantile regression,QR)模型,提出QR-CNN-Bi LSTM深度学习融合模型,计及筛选得到的多种气象因素,对光伏功率进行以5min为间隔的精细时间粒度分类区间预测,最后采用交叉验证和网格搜索方法的核密度估计给出概率密度预测结果。选取多种评价指标对提出的模型进行评价,并与QR-LSTM、QR-BiLSTM模型预测结果做对比分析,结果表明:1)FCM算法能有效实现光伏历史数据集的聚类;2)QR-CNN-BiLSTM融合模型能够生成以5min为间隔的高质量区间预测结果,95%置信预测区间综合评价指标平均值由QR-LSTM、QR-BiLSTM的0.1371、0.1288减小到0.0971;3)基于交叉验证和网格搜索方法的核密度估计能够实现可靠的光伏功率概率密度预测结果生成。

基于CNN-LSTM分位数回归的母线负荷日前区间预测

针对部分工业类母线负荷波动较大,传统点预测方法难以准确预测的问题,文中提出一种基于卷积神经网络(CNN)与长短期记忆网络分位数回归(QRLSTM)组合的母线负荷日前区间预测模型。首先,针对工业类负荷功率的高频波动,采用去噪自编码器对历史负荷数据进行降噪处理;然后,利用基于时间分布层封装的一维CNN网络进行负荷特征提取和压缩,以提升整个模型的学习效率;最后,建立含有注意力机制的QRLSTM模型进行特征学习,得到不同分位数下的负荷区间预测结果。对工业类和居民商业类2种典型的220 kV母线负荷进行了负荷日前区间预测测试,并与常规的分位数回归方法进行了对比。结果表明,文中方法获得的预测结果总体上区间覆盖率更大、区间平均宽度和区间累计偏差均更小,预测效果更好。

基于变分模态分解和分位数卷积-循环神经网络的短期风功率预测

由于风力发电的随机性和间歇性,风功率预测不仅需要准确的点预测,而且需要可靠的区间预测和概率预测来量化风功率的不确定性。提出了一种基于变分模态分解(variational mode decomposition,VMD)和分位数卷积-循环神经网络的风功率概率预测模型。首先,使用VMD技术将原始风功率数据序列分解为一系列特征互异的模态分量,再通过卷积神经网络(convolutional neural network,CNN)提取反映各模态分量动态变化的高阶特征。然后,基于提取的高阶特征进行分位数回归建模,采用长短期记忆(long shortterm memory,LSTM)循环神经网络预测未来任意时刻不同分位数条件下的风功率值。最后,利用核密度估计(kernel density estimation,KDE)得到风功率概率密度曲线。以中国某风电场数据作为算例测试,证明了所提出模型的有效性。