混沌局域法与神经网络组合供水量预测

城市供水量是非线性、非平稳时间序列,组合预测模型能获得更高精度预测结果。通过深入分析混沌局域法与神经网络预测模型特点,提出了一种新的组合预测模型。首先,应用混沌局域法对城市日供水量进行初预测,然后,应用神经网络对预测结果进行修正。由于所提出的组合模型利用了混沌局域法及神经网络进行优势互补,能同时提高预测精度与计算效率。为验证所提出组合预测模型的可行性,采用某市7a实测供水量数据,对混沌局域法、BPNN、RBF及GRNN神经网络4种单一预测模型及相应的3种组合模型预测精度进行定量分析,结果表明,组合预测模型精度都高于对应单一预测模型,混沌局域法与GRNN神经网络组合模型预测精度最高,且运算时间远低于单一神经网络模型运算时间。     

Using extreme learning machines for short-term urban water demand forecasting

This study explores the ability of various machine learning methods to improve the accuracy of urban water demand forecasting for the city of Montreal (Canada). Artificial Neural Network (ANN), Support Vector Regression (SVR) and Extreme Learning Machine (ELM) models, in addition to a traditional model (Multiple linear regression, MLR) were developed to forecast urban water demand at lead times of 1 and 3 days. The use of models based on ELM in water demand forecasting has not previously been explored in much detail. Models were based on different combinations of the main input variables (e.g., daily maximum temperature, daily total precipitation and daily water demand), for which data were available for Montreal, Canada between 1999 and 2010. Based on the squared coefficient of determination, the root mean square error and an examination of the residuals, ELM models provided greater accuracy than MLR, ANN or SVR models in forecasting Montreal urban water demand for 1 day and 3 days ahead, and can be considered a promising method for short-term urban water demand forecasting.     

An ARIMA-NARX hybrid model for forecasting urban water consumption (case study: Tehran metropolis)

ABSTRACT

In this research, an ARIMA-NARX (Autoregressive Integrated Moving Average-Nonlinear Auto-Regressive eXogenous) hybrid model is proposed to forecast daily Urban Water Consumption (UWC) for Tehran Metropolis. The linear and nonlinear component of the UWC was forecast by ARIMA as a linear forecasting model and the artificial neural network as a nonlinear forecasting model, respectively. An alternative hybrid model including sunshine hour in addition to the previous studies’ predictors (the minimum, maximum and average temperature, relative humidity and precipitation) was selected as the superior alternative model. Then, the performance of proposed model was compared with ARIMA and NARX models. The results showed that the hybrid model, which benefits from capability of both linear and nonlinear models, has a higher accuracy than the other two models in forecasting UWC. Therefore, the proposed hybrid model has better results in UWC forecasting and, as a consequence, better urban water reservoir management will be provided.     

An ensemble wavelet bootstrap machine learning approach to water demand forecasting: a case study in the city of Calgary,Canada

This paper explores a hybrid wavelet, bootstrap and neural network (WBNN) modeling approach for daily (1, 3 and 5 day) urban water demand forecasting in situations with limited data availability. This method was tested using 3 years of daily water demand and meteorological data for the city of Calgary, Alberta, Canada. The performance of the WBNN method was compared to that of three other methods: traditional neural networks (NN), wavelet NNs (WNN), and bootstrap-based NN (BNN) models. While the hybrid WBNN and WNN models equally provided 1-day lead-time forecasts of greater accuracy than those obtained with other methods, for longer lead-time (3- or 5-day) forecasts the WBNN model alone outperformed the other models. The confidence bands generated using the WBNN model displayed the uncertainty associated with the forecasts.     

基于级联神经网络的年降雨量预测

降雨量是农业生产的一个重要影响因素,如何准确预测降雨量成为指导农业、水利等一项重要的科技指标。从信息利用角度来看,单一预测模型仅能利用降雨量数据部分有效信息,而组合模型将单一模型的优势互补,可获得更佳的预测效果。基于神经网络理论的快速发展及级联神经网络预测模型被广泛应用于各个方面并取得了很好的结果,针对降雨量曲线的特点,深入分析BP神经网络及RBF神经网络发现,BP神经网络可很好的拟合对降雨量有很大影响的气候信息和其它因素,输出同一类型的降雨量影响信息;RBF网络的特点就是可很好地提取同一类信息特征,二者的组合可很大程度的提高降雨量预测精度。鉴于此,将BP-RBF级联神经网络引入降雨量预测研究中,实例计算表明,该方法高于单一神经网络预测精度,证明该方法应用于降雨量预测是合理有效的。     

A new method of ozone forecasting using fuzzy expert and neural network systems

This study describes the method of forecasting daily maximum ozone concentrations at four monitoring sites in Seoul, Korea. The forecasting tools developed are fuzzy expert and neural network systems. The hourly data for air pollutants and meteorological variables, obtained both at the surface and at the high elevation (500 hPa) stations of Seoul City for the period of 1989-1999, were analyzed. Two types of forecast models are developed. The first model, Part I, uses a fuzzy expert system and forecasts the possibility of high ozone levels (equal to or above 80 ppb) occurring on the next day. The second model, Part II, uses a neural network system to forecast the daily maximum concentration of ozone on the following day. The forecasting system includes a correction function so that the existing model can be updated whenever a new ozone episode appears. The accuracy of the forecasting system has been improved continuously through verification and augmentation.     

Assessment of predictive uncertainty within the framework of water demand forecasting using the Model Conditional Processor (MCP)

This paper presents an application of the Model Conditional Processor (MCP), originally proposed by Todini (2008) within the hydrological framework, to assess the predictive uncertainty in water demand forecasting related to water distribution systems. The MCP enables us to assess the probability distribution of the future water demand conditional on the forecasts provided by two or more deterministic forecasting models. In the numerical application described here, where two years of hourly water demand data for a town in northern Italy are considered, two forecasting models are applied in order to forecast hourly water demands from 1 to 24 hours ahead: the first model has a modular structure comprising a periodic component which reflects the long-term effects and a persistence component which represents the short-term memory of the process; the latter is based on neural networks. The results highlight the effectiveness of the approach, provided that the data set used for the MCP parameterization is properly selected so as to be actually representative of the accuracy of the real-time water demand forecasting models.     

混凝土氯离子扩散系数的RBF神经网络预测模型

提出了一种基于RBF神经网络的氯离子扩散系数预测模型,将RBF网络模型预测的结果与另外三种不同输入的RBF模型、BP网络模型的预测结果以及实测结果进行了对比分析,结果表明,RBF神经网络模型相对其他输入指标体系模型的预测精度有所提高,且能满足工程的需要,可以作为氯离子扩散系数预测的一种新的有效的方法。     

Monthly river flow forecasting by an adaptive neuro-fuzzy inference system

In this study, the applicability of an adaptive neuro-fuzzy inference system (ANFIS) to forecast for monthly river flows is investigated. For this, the Göksu river in the Seyhan catchment located in southern Turkey was chosen as a case study. The river flow forecasting models having various input structures are trained and tested by the ANFIS method. The results of ANFIS models for both training and testing are evaluated and the best-fit forecasting model is determined. The best-fit model is also trained and tested by feed forward neural networks (FFNN) and traditional autoregressive (AR) methods, and the performances of the models are compared. Moreover, ANFIS and FFNN models are verified by a validation data set including river flow data records during the time period 1997–2000. The results demonstrate that ANFIS can be applied successfully and provides high accuracy and reliability for monthly river flow forecasting.     

基于粒子群优化算法的城市日用水量预测模型

结合城市日用水量影响因素的特点和变化规律，建立了城市日用水量预测模型，采用粒子群优化算法优化BP人工神经网络的连接权值，以求解该预测模型。经优化后的BP人工神经网络运算速度快、泛化能力强、预测精度高。实例验证结果证明该日用水量预测模型和求解方法是可行的。     

地区间货物运输量预测方法谱系

本文整理了近些年地区间(城市间)货物运输量预测方法的研究。论文分析了和旅客运输相比,地区间货物运输的特点,提出了货物运输需求量的预测在理论和方法上和客流需求量的预测有所不同的依据。将以往的地区间货运量预测模型按不同阶段做了分类整理。其次,根据物流的行为者将地区间货运量预测模型分为计量经济学模型、空间相互作用模型及网络模型等类型。较为详尽地分析了各类预测模型的特点。并结合我国物流业的发展现状,对货物运输量预测方法研究的发展方向进行了展望。     

改进单指数平滑法预测某市日用水量

为实现供水管网经济、可靠、科学的优化调配用水量,给出一种基于改进单指数平滑预测方法,该预测方法引进"追踪信号"来反应时间序列的变化,通过重新修正平滑常数a以建立改进单指数预测模型。以东北某城市日用水量为原始数据进行了实际预测,模型精度检验的结果满足Y市用水量要求,该预测模型应用于Y市的日用水量预测,为Y市供水优化调配提供有效依据。     

Artificial neural network for forecasting rainfall pattern in Prince Edward Island,Canada

The forecast performance of alternative artificial neural network (ANN) models was studied to compare their forecast accuracy to the fractionally integrated autoregressive moving average model using monthly rainfall data for the Canadian province of Prince Edward Island (PEI). A multilayer feed-forward back-propagation ANN algorithm is implemented to evaluate the forecast accuracy and to analyse the statistical characteristics of the ANN model for original data and for data pre-processed with moving average and exponential smoothing transformations. The prediction performance of these models is compared to that of a seasonal autoregressive fractionally integrated moving average time series model. The statistical results show that the ANN model with exponential smoothing of the data has the smallest root mean square error and the highest correlation coefficient and thus, outperforms the alternative models investigated in this study.     

基于小波分解的城市供水管网短期水量负荷预测

利用小波分解和人工神经网络相结合的方法建立了城市供水管网短期水量负荷的组合预测模型。该方法首先利用小波分解技术将时负荷水量分解为相对简单的带通分量信号，然后根据各分量信号的特点分别建立独立的神经网络预测模型，最后将预报结果集成。由于小波分解后各分量的频率相对单一，因而可有效缩短网络训练时间，提高计算速度。仿真计算结果表明，该方法建模合理、计算量适中，可准确预测管网水量。     

基于灰色RBF组合模型的城市用水量预测

为解决城市用水量预测中单一方法预测精度不高的问题,建立了灰色径向基（RBF）神经网络组合模型。对比实验结果表明,灰色GM（1,1）模型、RBF神经网络模型和灰色RBF神经网络组合模型的平均相对误差分别为2．1222％,1．2562％和0．6821％。与灰色GM（1,1）模型和RBF神经网络相比,灰色RBF神经网络组合模型充分发挥了灰色系统的贫乏数据建模和RBF神经网络的高度非线性映射能力的双重优势,具有较高的预测精度,更适合用于城市用水量预测。     

基于时空序列模型的RBF神经网络在河流水位预测中的应用

河流水位预测一直以来都是水文预报中研究的热点。河流水位变化不定,具有时间上和空间上的变化性、多维性、动态性和不确定性等,给水位预测带来了挑战。本文综合考虑河流水位时空信息,建立基于时空序列的RBF神经网络预测模型来预测河流水位。实验中预测了金沙江下游向家坝水文站的水位信息,并将实验结果与其他多种水位预测方法比较,实验结果显示基于时空序列的RBF神经网络模型在河流水位预测中具有较高精度,证明了方法的可行性。     

忠武输气管道沿线滑坡预测方法研究

基于忠武管道沿线滑坡不具有成面、成片分布的特点及其管道沿线滑坡监测方案,系统地分析了忠武输气管道沿线滑坡预测过程,根据灰色GM(1,1)模型具有对数据量需求少、对时间有关的序列有很好的预测效果等优点,重点研究了灰色GM(1,1)模型及其改进模型,并对各种改进的灰色模型预测结果进行比较分析;同时顾及BP神经网络的各种优点...     

利用改进的BP神经网络预测烧结砖的抗压强度

根据改进的BP神经网络基本原理，建立了烧结砖的神经网络强度预测模型．利用试验数据训练神经网络，通过工程实例，对训练过的神经网络进行了测试，并将用神经网络方法获得的结果与用传统数学回归模型计算的结果进行了对比．结果表明，该神经网络方法所得的预测值优于传统方法．     

局域法邻近点选取对供水量预测精度的影响

混沌局域法预测模型适用于非线性、非平稳的城市日供水量预测,而邻近相点个数的选取对该模型预测精度有直接影响。传统方法通常以嵌入维m作为参考值,凭经验选取m+1个邻近相点,且仅使用欧式距离法计算当前相点距离,无法反映相点的运动趋势,易引入伪邻近相点,导致预测精度的降低。鉴于此,将演化追踪法引入城市日供水量预测,通过挖掘邻近相点的历史演化规律对参考样本进行优选,以提高预测精度。最后,采用实际日供水量数据验证所提出方法,结果表明,运用演化追踪法优选邻近相点能显著提高日供水量预测精度,预测平均绝对误差由2.501%降低到1.683%。     

Applying support vector machines to predict building energy consumption in tropical region

The methodology to predict building energy consumption is increasingly important for building energy baseline model development and measurement and verification protocol (MVP). This paper presents support vector machines (SVM), a new neural network algorithm, to forecast building energy consumption in the tropical region. The objective of this paper is to examine the feasibility and applicability of SVM in building load forecasting area. Four commercial buildings in Singapore are selected randomly as case studies. Weather data including monthly mean outdoor dry-bulb temperature (T₀), relative humidity (RH) and global solar radiation (GSR) are taken as three input features. Mean monthly landlord utility bills are collected for developing and testing models. In addition, the performance of SVM with respect to two parameters, C and ɛ, was explored using stepwise searching method based on radial-basis function (RBF) kernel. Finally, all prediction results are found to have coefficients of variance (CV) less than 3% and percentage error (%error) within 4%.