民用建筑供热负荷的神经网络法预测

分析了供热系统负荷变化的各种扰量,提出利用人工神经网络对供热负荷进行预测的方法。对神经网络预测的可行性、方法的实施内容及输入输同变量的选择,网络连接方法的选择等进行了讨论。在进一步对供热负荷特性研究的基础上,可以利用人工神经网络对其进行切实可行的预测。     

人工神经网络在商场建筑物冷负荷预测中的应用

对空调系统进行冷负荷预测是对冰蓄冷系统进行优化控制的重要前提与基础。经过对各种预测方法的结果比较 ,人们发现人工神经网络预测的结果更接近实际值。基于人工神经网络的通用BP( Back Propagation)算法编制的程序 ,实际对一栋采用冰蓄冷空调的商场性质建筑物进行冷负荷预测并加以评价。此程序采用 Visual Basic编制 ,含有 7个输入层以及 1个输出层 ,利用通用 BP算法。结果显示利用人工神经网络预测建筑物冷负荷比较可靠。     

New artificial neural network prediction method for electrical consumption forecasting based on building end-uses

Due to the current high energy prices it is essential to find ways to take advantage of new energy resources and enable consumers to better understand their load curve. This understanding will help to improve customer flexibility and their ability to respond to price or other signals from the electricity market. In this scenario, one of the most important steps is to carry out an accurate calculation of the expected consumption curve, i.e. the baseline. Subsequently, with a proper baseline, customers can participate in demand response programs and verify performed actions. This paper presents an artificial neural network (ANN) method for short-term prediction of total power consumption in buildings with several independent processes. This problem has been widely discussed in recent literature but a new point of view is proposed. The method is based on two fundamental features: total consumption forecast based on independent processes of the considered load or end-uses; and an adequate selection of the training data set in order to simplify the ANN architecture. Validation of the method has been performed with the prediction of the whole consumption expressed as 96 active energy quarter-hourly values of the Universitat Politècnica de València, a commercial customer consuming 11,500 kW.     

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based artificial neural network

In the recent era, piled raft foundation (PRF) has been considered an emergent technology for offshore and onshore structures. In previous studies, there is a lack of illustration regarding the load sharing and interaction behavior which are considered the main intents in the present study. Finite element (FE) models are prepared with various design variables in a double-layer soil system, and the load sharing and interaction factors of piled rafts are estimated. The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificial neural network (ANN) modeling, and some prediction models are proposed. ANN models are prepared with Levenberg–Marquardt (LM) algorithm for load sharing and interaction factors through backpropagation technique. The factor of safety (FS) of PRF is also estimated using the proposed NMR and ANN models, which can be used for developing the design strategy of PRF.     

热负荷预测方法评析

在深入研究目前常用的供热负荷预测方法的基础上，对热负荷预测方法进行了科学的分类，重点评析了ARMA、回归分析法，灰色预测方法，人工神经网络方法的优缺点及适用条件，并对热计量供热系统的负荷预测方法进行了探讨。     

Assessing artificial neural network performance for predicting interlayer conditions and layer modulus of multi-layered flexible pavement

The objective of this study is to evaluate the performance of the artificial neural network (ANN) approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure. To achieve this goal, two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure. The corresponding database built with ANSYS based finite element method computations for four types of a structure subjected to falling weight deflectometer load. In addition, two proposed ANN models were verified by comparing the results of ANN models with the results of PADAL and double multiple regression models. The measured pavement deflection basin data was used for the verifications. The comparing results concluded that there are no significant differences between the results estimated by ANN and double multiple regression models. PADAL modeling results were not accurate due to the inability to reflect the real pavement structure because pavement structure was not completely continuous. The prediction and verification results concluded that the proposed back-calculation model developed with ANN could be used to accurately predict layer modulus and interlayer conditions. In addition, the back-calculation model avoided the back-calculation errors by considering the interlayer condition, which was barely considered by former models reported in the published studies.     

Long-term deflection of cracked composite beams with nonlinear partial shear interaction — A study using neural networks

A new study of the short- and long-term deflections of simply-supported composite beams using finite element analysis and artificial neural networks (ANNs) is presented. In this study, two ANN models are developed and trained using the results of a finite element model developed by the authors in a companion paper. The finite element model accounted for the nonlinear load–slip relationship of shear connectors as well as the creep, shrinkage, and cracking of concrete slabs. The effects of creep and shrinkage of the concrete slab are considered only for non-cracked concrete. A large database representing a wide range of different design parameters was constructed for the purpose of training and verifying the two ANN models. It was found that the two ANN models were capable of predicting deflections of composite beams not used as part of the training process. The ANN models were then used to evaluate the effects of non-geometric design variables on the short- and long-term deflections of simply-supported composite beams. Finally, the short- and long-term deflections computed based on the approaches given in the AISC specification and Eurocode 4 were assessed using the results of the finite element model. It was found that the AISC approach underestimates short-term deflections and overestimate long-term deflections when compared with the results of the finite element method.     

基于神经网络的供热计量系统热负荷短期预测

针对实行热计量后热负荷变化的特点，采用神经网络中应用最广泛的BP网络对热负荷进行预测，利用MATLAB仿真程序对所建立的人工神经网络负荷预测模型进行验证，仿真误差为6．93％，满足工程需要。     

A generic method for rock mass classification

Rock mass classification (RMC) is of critical importance in support design and applications to mining, tunneling and other underground excavations. Although a number of techniques are available, there exists an uncertainty in application to complex underground works. In the present work, a generic rock mass rating (GRMR) system is developed. The proposed GRMR system refers to as most commonly used techniques, and two rock load equations are suggested in terms of GRMR, which are based on the fact that whether all the rock parameters considered by the system have an influence or only few of them are influencing. The GRMR method has been validated with the data obtained from three underground coal mines in India. Then, a semi-empirical model is developed for the GRMR method using artificial neural network (ANN), and it is validated by a comparative analysis of ANN model results with that by analytical GRMR method.     

神经网络法负荷预测与蓄冰空调系统的运行优化

文中介绍了蓄冰空调系统几种常见的控制策略。提出蓄冰空调系统的运行优化必须进行准确的负荷预测,并给出采用神经网络模型(ANN) 预测负荷的方法     

前馈网络升级策略及其复合射孔岩层裂深自适应动态升级预测模型

油井复合射孔岩层裂缝深度预测模型的研究具有广阔的应用前景。在已有的复合射孔岩层裂深神经网络预测模型基础上，通过对前馈逆传播网络算法的研究，采用样本出现概率自适应控制方法，初步提出基于样本分类的单隐层网络结构的知识升级策略，给出了具体算法，并建立了油并复合射孔岩层裂深预测的神经网络自适应网络知识升级模型。完成后的模型能够针对样本空间的局部更新作出自适应调整，从而实现对神经网络结构所掌握知识的动态升级。为解决前馈神经网络建模中的样本变更问题提供了一条新的途径。研究发现，这种前馈网络升级策略具有一定的普适性，可用于其他岩土工程神经网络建模问题。     

人工神经网络在暖通空调领域的应用研究发展

综述了人工神经网络在暖通空调领域的研究和开发现状,阐述了负荷预测、能量管理、故障诊断、系统辨识与控制等应用方面,展望了进一步的研究方向。     

基坑变形预测的时间序列分析

在分析灰色系统与神经网络基本原理的基础上,结合前人研究成果和实例分析,提出灰色系统用于基坑变形预测存在的一些问题,认为灰色系统不宜用于地下连续墙水平位移的预测,在其它变形预测中也要慎用.建立了基坑变形预测的神经网络模型,并用实例加以论证.研究表明,神经网络是解决基坑变形预测的有效方法,在地下工程中具有很好的应用前景.     

An intelligent approach to assessing the effect of building occupancy on building cooling load prediction

Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry have been developed from either forward or inverse modeling approaches. However, these models usually require extensive computer resources and lengthy computation. This paper discusses the use of the multi-layer perceptron (MLP) model, one of the artificial neural network (ANN) models widely adopted in engineering applications, to estimate the cooling load of a building. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing prestigious commercial building in Hong Kong that houses a mega complex and operates 24 h a day. The paper also discusses the practical difficulties encountered in acquiring building-related data. In contrast to other studies that use ANN models to predict building cooling load, this paper includes the building occupancy rate as one of the input parameters used to determine building cooling load. The results demonstrate that the building occupancy rate plays a critical role in building cooling load prediction and significantly improves predictive accuracy.     

Development of Recurrent Neural Network Considering Temporal‐Spatial Input Dynamics for Freeway Travel Time Modeling

Abstract: The artificial neural network (ANN) is one advance approach to freeway travel time prediction. Various studies using different inputs have come to no consensus on the effects of input selections. In addition, very little discussion has been made on the temporal–spatial aspect of the ANN travel time prediction process. In this study, we employ an ANN ensemble technique to analyze the effects of various input settings on the ANN prediction performances. Volume, occupancy, and speed are used as inputs to predict travel times. The predictions are then compared against the travel times collected from the toll collection system in Houston. The results show speed or occupancy measured at the segment of interest may be used as sole input to produce acceptable predictions, but all three variables together tend to yield the best prediction results. The inclusion of inputs from both upstream and downstream segments is statistically better than using only the inputs from current segment. It also appears that the magnitude of prevailing segment travel time can be used as a guideline to set up temporal input delays for better prediction accuracies. The evaluation of spatiotemporal input interactions reveals that past information on downstream and current segments is useful in improving prediction accuracy whereas past inputs from the upstream location do not provide as much constructive information. Finally, a variant of the state‐space model (SSNN), namely time‐delayed state‐space neural network (TDSSNN), is proposed and compared against other popular ANN models. The comparison shows that the TDSSNN outperforms other networks and remains very comparable with the SSNN. Future research is needed to analyze TDSSNN's ability in corridor prediction settings.     

人工神经网络在工业污染源综合评价中的应用

现有污染源评价方法很多，但均为线性方程，由于环境、经济、社会指标间相互关系非常复杂，既存在多重共线性，又存在非线性关系，这使得应用现有方法对污染源进行环境经济属性综合评价时难以做到周全。以深圳市为例，首次引入人工神经网络（ANN）对工业行业污染源进行综合评价，并采用等标污染负荷和综合判别指数两种方法进行对比。结果表明，ANN法的评价结果最优。由于ANN具有模拟任意非线性连续函数的能力及自学习的特点，因此适用于污染源的综合评价。     

Neural network-based model for prediction of permanent deformation of unbound granular materials

Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation (PD) of unbound granular materials (UGMs), which make these methods more conservative. In addition, there are limited regression models capable of predicting the PD under multi-stress levels, and these models have regression limitations and generally fail to cover the complexity of UGM behaviour. Recent researches are focused on using new methods of computational intelligence systems to address the problems, such as artificial neural network (ANN). In this context, we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads. Extensive repeated load triaxial tests (RLTTs) were conducted on base and subbase materials locally available in Victoria, Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks. Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix. The ANN model consists of one input layer with five neurons, one hidden layer with twelve neurons, and one output layer with one neuron. The five inputs were the number of load cycles, deviatoric stress, moisture content, coefficient of uniformity, and coefficient of curvature. The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%. It shows that the ANN method is rapid and efficient to predict the PD, which could be implemented in the Austroads pavement design method.     

A new application area of ANN and ANFIS: determination of earthquake load reduction factor of prefabricated industrial buildings

The earthquake load reduction factor, R, is one of the most important parameters in the design stage of a building. Significant damages and failures were experienced on prefabricated reinforced concrete structures during the last earthquakes in Turkey and the experts agreed that they resulted mainly from the incorrectly selected earthquake load reduction factor, R. In this study, an attempt was made to estimate the R coefficient for prefabricated industrial structures having a single storey, one and two bays, which are commonly constructed for manufacturing and warehouse operation with variable dimensions. According to the selected variable dimensions, 280 sample (140 samples for one bay (S-1) and 140 samples for two bays (S-2)) frames’ load–displacement relations were computed using pushover analysis and the earthquake load reduction factor, R, was calculated for each frame. Then, formulated three-layered artificial neural network methods (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) were trained by using 214 of the 280 sample frames. Then, the methods were tested with the other 66 sample frames. Accuracy rates were found to be about 94% and 96% for ANN and ANFIS, respectively. The use of ANN and ANFIS provided an alternative way for estimating the R and it also showed that ANFIS estimated R more successfully than ANN.     

Multiobjective optimization of building design using TRNSYS simulations,genetic algorithm,and Artificial Neural Network

Building optimization involving multiple objectives is generally an extremely time-consuming process. The GAINN approach presented in this study first uses a simulation-based Artificial Neural Network (ANN) to characterize building behaviour, and then combines this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization. The methodology has been used in the current study for the optimization of thermal comfort and energy consumption in a residential house. Results of ANN training and validation are first discussed. Two optimizations were then conducted taking variables from HVAC system settings, thermostat programming, and passive solar design. By integrating ANN into optimization the total simulation time was considerably reduced compared to classical optimization methodology. Results of the optimizations showed significant reduction in terms of energy consumption as well as improvement in thermal comfort. Finally, thanks to the multiobjective approach, dozens of potential designs were revealed, with a wide range of trade-offs between thermal comfort and energy consumption.     

人工神经网络在空调系统负荷预测中的应用

采用理论分析的方法，通过分析国内外在该方面的研究成果，剖析了人工神经网络在空调系统负荷预测中的应用，指出了利用人工神经网络（ANN）具有的高度的并行处理和可完成复杂的输入输出的非线性映射能力，进行空调系统负荷预测精度高、准确度好。ANN是一种有效的空调负荷预测手段。