Improved estimation of electricity demand function by using of artificial neural network,principal component analysis and data envelopment analysis

Due to various seasonal and monthly changes in electricity consumption and difficulties in modeling it with the conventional methods, a novel algorithm is proposed in this paper. This study presents an approach that uses Artificial Neural Network (ANN), Principal Component Analysis (PCA), Data Envelopment Analysis (DEA) and ANOVA methods to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption. Pre-processing and post-processing techniques in the data mining field are used in the present study. We analyze the impact of the data pre-processing and post-processing on the ANN performance and a 680 ANN-MLP is constructed for this purpose. DEA is used to compare the constructed ANN models as well as ANN learning algorithm performance. The average, minimum, maximum and standard deviation of mean absolute percentage error (MAPE) of each constructed ANN are used as the DEA inputs. The DEA helps the user to use an appropriate ANN model as an acceptable forecasting tool. In the other words, various error calculation methods are used to find a robust ANN learning algorithm. Moreover, PCA is used as an input selection method, and a preferred time series model is chosen from the linear (ARIMA) and nonlinear models. After selecting the preferred ARIMA model, the Mcleod–Li test is applied to determine the nonlinearity condition. Once the nonlinearity condition is satisfied, the preferred nonlinear model is selected and compared with the preferred ARIMA model, and the best time series model is selected. Then, a new algorithm is developed for the time series estimation; in each case an ANN or conventional time series model is selected for the estimation and prediction. To show the applicability and superiority of the proposed ANN-PCA-DEA-ANOVA algorithm, the data regarding the Iranian electricity consumption from April 1992 to February 2004 are used. The results show that the proposed algorithm provides an accurate solution for the problem of estimating electricity consumption.     

A new hybrid method for time series forecasting: AR–ANFIS

In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR–ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR–ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.

     

混沌时间序列的自适应变异差分进化ANFIS预测

提出了一种基于自适应变异差分进化（AMDE）算法的ANFIS模型对混沌时间序列进行预测的方法,该方法采用自适应变异差分进化算法和最小二乘法相结合的混合学习算法对ANFIS网络结构参数进行优化设计,利用差分进化算法的全局寻优能力对ANFIS网络前件参数进行优化,而网络的结论参数采用最小二乘法优化,混合学习算法提高了网络参数辨识的收敛速度和系统的全局收敛性,仿真实验结果表明了该方法的有效性。     

Identification using ANFIS with intelligent hybrid stable learning algorithm approaches

This paper suggests novel hybrid learning algorithm with stable learning laws for adaptive network based fuzzy inference system (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and gradient descent (GD) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. This paper, studies the stability of PSO as an optimizer in training the identifier, for the first time. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data.     

基于Bootstrap采样粒子滤波的WirelessHART时间同步算法﹡

针对无线HART传感器网络时间同步精度较低、能耗过大等问题,提出了一种基于Bootstrap采样的粒子滤波时间同步算法。在未知网络延迟分布的情况下,为了减少观测次数,对发送端和接收端的时间戳观测值进行Bootstrap采样,采用混合粒子滤波算法,获得精确的时钟偏移,从而不仅降低了无线HART传感网络时间同步误差,而且使能耗减小。最后,实验表明,对于无线HART网状分层网络,当观测量达到10以上时,粒子滤波算法获得的时间偏差的均方误差约是最大似然估计算法的50%,而基于Bootstrap采样的粒子滤波算法获得的时间偏差的均方误差约是最大似然估计算法的35%,仿真的结果验证了该方法的可行性和有效性。     

A hybrid genetic algorithm for the electric vehicle routing problem with time windows

Driven by the newlegislation on greenhouse gas emissions, carriers began to use electric vehicles (EVs) for logistics transportation. This paper addresses an electric vehicle routing problem with time windows (EVRPTW). The electricity consumption of EVs is expressed by the battery state-of-charge (SoC). To make it more realistic, we take into account the terrain grades of roads, which affect the travel process of EVs. Within our work, the battery SoC dynamics of EVs are used to describe this situation. We aim to minimize the total electricity consumption while serving a set of customers. To tackle this problem, we formulate the problem as a mixed integer programming model. Furthermore, we develop a hybrid genetic algorithm (GA) that combines the 2-opt algorithm with GA. In simulation results, by the comparison of the simulated annealing (SA) algorithm and GA, the proposed approach indicates that it can provide better solutions in a short time.     

减法聚类-ANFIS在网络故障诊断的应用研究

提出了一种基于减法聚类-自适应模糊神经网络（ANFIS）的网络故障诊断建模方法。减法聚类算法生成初始模糊推理系统,ANFIS建立网络故障诊断原始模型,应用混合算法对模糊规则的参数进行训练并建立最终的模型。仿真实验表明基于减法聚类-ANFIS的建模方法是有效的;通过仿真结果比较,减法聚类-ANFIS的网络故障诊断能力及收敛速度均优于BP神经网络,更适合作为网络故障诊断模型。     

Application of ANFIS Model for Thailand’s Electric Vehicle Consumption

Generally, road transport is a major energy-consuming sector. Fuel consumption of each vehicle is an important factor that affects the overall energy consumption, driving behavior and vehicle characteristic are the main factors affecting the change of vehicle fuel consumption. It is difficult to analyze the influence of fuel consumption with multiple and complex factors. The Adaptive Neuro-Fuzzy Inference System (ANFIS) approach was employed to develop a vehicle fuel consumption model based on multivariate input. The ANFIS network was constructed by various experiments based on the ANFIS Parameter setting. The performance of the ANFIS network was validated using Root Mean Square Error (RMSE) and Mean Average Error (MAE) which related to the setting of ANFIS parameters. The experimental results indicated that the training data sample, number, and type of membership functions are the most important factor affecting the performance of the ANFIS network. However, the number of epochs does not necessarily significantly improve the system performance, too many the number of epochs setting may not provide the best results and lead to excessive responding time. The results also demonstrate that three factors, consisted of the engine size, driving speed, and the number of passengers, are important factors that influence the change of vehicle fuel consumption. The selected ANFIS models with minimum error can be properly and efficiently used to predict vehicle fuel consumption for Thailand’s road transport sector.     

嵌入式软件算法级能耗建模与分析

针对嵌入式软件算法级能耗的优化问题,建立算法级能耗估算模型。以旅行商问题(TSP)为例,采用神经网络算法、遗传算法等进行能耗求解,对求解过程中的算法执行次数、算法复杂度以及运行时间这3个特征值进行能耗分析,通过能耗估算模型计算出算法针对TSP问题的能耗估算值,并将该估算值与使用sim-panalyzer功耗仿真平台求解得到的能耗测试值进行比较,结果表明,能耗测试值与估算值的误差在10%左右,证明该能耗估算模型具有较高的准确性。     

基于减法聚类模糊推理系统的短期负荷预测

采用减法聚类辅助模糊推理系统进行电力系统短期负荷预测。首先用减法聚类建立T-S模糊模型,然后通过调整聚类半径优选模糊规则数,以取得具有良好泛化性能的模型,最后利用梯度下降混合最小二乘算法精调参数。利用某局网负荷数据对ANFIS网络模型进行训练和检测,然后用于负荷预测,所得结果表明该算法鲁棒性好,抗干扰能力强,并且预测时间较ANFIS大大减少。     

基于传感器网络的1比特RLS算法

在基于传感器网络的参数估计中,如何尽可能降低网络的使用成本,同时又能获得较好的参数估计性能,是近年来受到国内外学者广泛关注的一个研究问题。为了减小网络的能量消耗和节省带宽、存储资源,本文考虑将传感器网络中每个节点的测量值压缩成1比特数据,然后将其传输到中心节点进行集中处理,并在此基础上提出了一种基于期望最大和递归最小二乘的自适应参数估计算法。论文通过一系列MATLAB仿真实验,验证了该算法具有较好的收敛性和鲁棒性,并能获得与使用非量化测量值的经典RLS算法相近的估计精度。     

Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) model for estimation of vigilance level by using electroencephalogram (EEG) signals recorded during transition from wakefulness to sleep. The developed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. This study comprises of three stages. In the first stage, three types of EEG signals (alert signal, drowsy signal and sleep signal) were obtained from 30 healthy subjects. In the second stage, for feature extraction, obtained EEG signals were separated to its sub-bands using discrete wavelet transform (DWT). Then, entropy of each sub-band was calculated using Shannon entropy algorithm. In the third stage, the ANFIS was trained with the back-propagation gradient descent method in combination with least squares method. The extracted features of three types of EEG signals were used as input patterns of the three ANFIS classifiers. In order to improve estimation accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The performance of the ANFIS model was tested using the EEG data obtained from 12 healthy subjects that have not been used for the training. The results confirmed that the developed ANFIS classifier has potential for estimation of vigilance level by using EEG signals.     

基于VAR的电能表降维误差估计模型

为达到远程监控电能表状态并及时准确发现电能表异常的目的,本文提出了一种基于VAR的电能表降维误差估计模型,通过对于电能表电量数据的获取、分析及筛选,采用主成分分析方法（PCA）对于原始数据进行降维,通过向量自回归模型（VAR）提取时间序列中的特征,从而准确预测电能表使用电量并对比出用电异常电能表。其中PCA降维算法处理了实际模型的不可解性,VAR自回归算法提高了估计的稳定性和精度,相较于传统方法具有预测准确度高,所需数据量小的特点。为验证该方法的有效性和实用性,将该方法应用于实际台区中测试,通过对于台区中127块电表半年内的用电数据进行分析,准确定位出8个异常电能表。结果表明,该方法不需要提前独立计算网损,能够实时估计智能电表误差和网损率。     

Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods

This paper proposes a novel hybrid learning algorithm with stable learning laws for Adaptive Network based Fuzzy Inference System (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and forgetting factor recursive least square (FFRLS) for training the conclusion part. Two famous training algorithms for ANFIS are the gradient descent (GD) to update antecedent part parameters and using GD or recursive least square (RLS) to update conclusion part parameters. Lyapunov stability theory is used to study the stability of the proposed algorithms. This paper, also studies the stability of PSO as an optimizer in training the identifier. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data. Also, stable learning algorithms for two common methods are proposed based on Lyapunov stability theory and some constraints are obtained.     

Prediction of chaotic time series using computational intelligence

In this paper, two CI techniques, namely, single multiplicative neuron (SMN) model and adaptive neuro-fuzzy inference system (ANFIS), have been proposed for time series prediction. A variation of particle swarm optimization (PSO) with co-operative sub-swarms, called COPSO, has been used for estimation of SMN model parameters leading to COPSO-SMN. The prediction effectiveness of COPSO-SMN and ANFIS has been illustrated using commonly used nonlinear, non-stationary and chaotic benchmark datasets of Mackey–Glass, Box–Jenkins and biomedical signals of electroencephalogram (EEG). The training and test performances of both hybrid CI techniques have been compared for these datasets.     

基于人工蜂群算法和XGBoost的网络入侵检测方法研究

为了降低网络入侵检测系统的虚警率,提出一种混合式网络入侵检测方法,将人工蜂群(ABC)算法用于特征提取,XGBoost算法用于特征分类和评价。选择和定义不同的场景和攻击类型,并设计混合式网络拓扑;对预处理后的数据,采用ABC算法进行特征提取,利用XGBoost算法将需要评价的特征进行分类;得到特征的最优子集,利用这些特征完成网络异常检测。在多个公开数据集上的实验结果表明,该混合方法在准确度和检测率方面优于其他方法,且其时间复杂度和空间复杂度较低,表现出较高的检测效率。     

基于粒子滤波算法的混合系统监测与诊断

利用粒子滤波算法具有同时估计连续状态和离散状态的特点,提出一种可用于混合系 统状态监测与诊断的新方法.给出了该方法的理论推导和设计步骤,讨论了在诊断应用中粒子滤 波器所遇到的问题,并给出了改善的措施.仿真结果证明用粒子滤波器对混合系统进行监测与诊 断是可行的,所提的方法对估计结果有比较好的改善.     

基于时序分解的用电负荷分析与预测

在智能电网普及的大数据背景下,对电力数据进行精准的分析和预测对电网规划和经济部门的管理决策具有重要的指导意义,但大多数模型都只是在单一的时间尺度上进行研究。针对这一问题提出一种基于时序分解的后向传播算法的循环神经网络预测模型。通过对真实的居民用电消费数据以及外部因素数据统计处理,深入地分析了居民用电特点以及行为规律,并根据其数据的特征以及天气、节假日等外部因素对用户用电行为的影响建立预测模型,对用户未来时段的用电量进行预测。此外,考虑到居民用电消费数据的时序特征在不同时间尺度呈现不同的变化规律,通过时序分解建立预测模型来对用户用电行为的周期性和趋势性进行建模,并通过加权融合达到一起训练的效果,具有一定的协同性,提升预测精度。     

基于自适应神经模糊推理系统的噪声消除方法

针对信号处理领域噪声消除的实际问题,提出了一种基于模糊推理的自适应神经网络控制方法.通过自适应神经模糊推理系统(ANFIS)对非线性系统的结构和参数进行辨识与自学习,采用混合学习算法,对前向参数和结论参数分别辨识,在提高精度的同时可加快训练收敛的速度,使控制系统具有良好动静态性和鲁棒性,实现了消除通信系统中噪声的目标,最后对基于ANFIS的噪声消除系统进行了建模和仿真,并与自适应神经网络滤波方法的结果对比,其结果证明了该方法的有效性.     

Design of decision model for sensitive crop irrigation system

Agriculture Industry is highly dependent on environmental and weather conditions. Many times, crops are spoiled because of sudden changes in weather. Therefore, we need a decision model to take care the water requirement of sensitive crops of agriculture industry. The proposed work presents a novel and proficient hybrid model for sensitive crop irrigation system (SCIS). For implementation of the model, brassica crop is taken. The duration and amount of water to be supplied is based upon the weather prediction and soil condition information. The decision model is developed using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) for brassica crops. In this model, if the input data values are available in range, then ANFIS model would be preferred and if the data sets are available for training, testing and validation then ANN model would be the best choice. The soil moisture, soil status in terms of temperature and leaf wetness are the input and flow control of sprinklers is the out for SCIS. The predicted outputs are analysed to assert the suitability of the proposed approach in the brassica crops. The proposed SCIS achieved an accuracy of 91% and 99% for ANFIS and ANN models respectively.