期刊文献+
共找到1,382篇文章
< 1 2 70 >
每页显示 20 50 100
Taxi origin and destination demand prediction based on deep learning:a review
1
作者 Dan Peng Mingxia Huang Zhibo Xing 《Digital Transportation and Safety》 2023年第3期176-189,共14页
Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications... Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions. 展开更多
关键词 Deep learning Taxi demand prediction Taxi OD demand prediction Spatiotemporal data mining Dynamic graph
下载PDF
Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction
2
作者 Subhajit Chatterjee Yung-Cheol Byun 《Computers, Materials & Continua》 SCIE EI 2023年第3期5507-5525,共19页
The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist... The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features.Electric kickboards are gradually growing in popularity in tourist and education-centric localities.In the upcoming arrival of electric kickboard vehicles,deploying a customer rental service is essential.Due to its freefloating nature,the shared electric kickboard is a common and practical means of transportation.Relocation plans for shared electric kickboards are required to increase the quality of service,and forecasting demand for their use in a specific region is crucial.Predicting demand accurately with small data is troublesome.Extensive data is necessary for training machine learning algorithms for effective prediction.Data generation is a method for expanding the amount of data that will be further accessible for training.In this work,we proposed a model that takes time-series customers’electric kickboard demand data as input,pre-processes it,and generates synthetic data according to the original data distribution using generative adversarial networks(GAN).The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data.We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results.We modified The Wasserstein GAN-gradient penalty(GP)with the RMSprop optimizer and then employed Spectral Normalization(SN)to improve training stability and faster convergence.Finally,we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction.We used various evaluation criteria and visual representations to compare our proposed model’s performance.Synthetic data generated by our suggested GAN model is also evaluated.The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem,and it also converges faster than previous GAN models for synthetic data creation.The presented model’s performance is compared to existing ensemble and baseline models.The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error(MAPE)of 4.476 and increase prediction accuracy. 展开更多
关键词 Machine learning generative adversarial networks electric vehicle time-series TGAN WGAN-GP blend model demand prediction regression
下载PDF
Demand prediction and purchase optimization decision model for alloys in steel making
3
作者 JIA Shujin YI Jian +1 位作者 WEN Jing DU Bin 《Baosteel Technical Research》 CAS 2022年第4期33-39,共7页
In this study,related models of alloy purchasing decision system in the Baoshan base of Baosteel are discussed.First,the corresponding relationship between steel grades and alloy consumption is established through met... In this study,related models of alloy purchasing decision system in the Baoshan base of Baosteel are discussed.First,the corresponding relationship between steel grades and alloy consumption is established through metallurgical-mechanism modeling and statistical analysis.Then,the alloy-demand prediction model based on alloy unit consumption and time series analysis is developed by combining sales plans and historical data.Finally,the alloy purchasing and inventory optimization model is developed to minimize the total cost of purchase and storage by combining inventory optimization theories. 展开更多
关键词 demand prediction alloy purchase intelligent optimization decision system
下载PDF
A Combination Prediction Model for Short Term Travel Demand of Urban Taxi
4
作者 Mingyuan Li Yuanli Gu +1 位作者 Qingqiao Geng Hongru Yu 《Computers, Materials & Continua》 SCIE EI 2024年第6期3877-3896,共20页
This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.Th... This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting. 展开更多
关键词 Urban transport taxi travel demand prediction CEEMDAN-ConvLSTM modal components
下载PDF
Ride-hailing origin-destination demand prediction with spatiotemporal information fusion
5
作者 Ning Wang Liang Zheng +1 位作者 Huitao Shen Shukai Li 《Transportation Safety and Environment》 EI 2024年第2期63-74,共12页
Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand,and improving the service level of ride-hailing platforms.In contrast to previous studies,which have primarily foc... Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand,and improving the service level of ride-hailing platforms.In contrast to previous studies,which have primarily focused on the inflow or outflow demands of each zone,this study proposes a conditional generative adversarial network with a Wasserstein divergence objective(CWGAN-div)to predict ride-hailing origin-destination(OD)demand matrices.Residual blocks and refined loss functions help to enhance the stability of model training.Interpretable conditional information is employed to capture external spatiotemporal dependencies and guide the model towards generating more precise results.Empirical analysis using ride-hailing data from Manhattan,New York City,demon-strates that our proposed CWGAN-div model can effectively predict the network-wide OD matrix and exhibits strong convergence performance.Comparative experiments also show that the CWGAN-div outperforms other benchmarking methods.Consequently,the proposed model displays potential for network-wide ride-hailing OD demand prediction. 展开更多
关键词 intelligent transport system ride-hailing generative adversarial networks spatiotemporal dependencies origin-destination(OD)demand prediction
原文传递
Understanding the demand predictability of bike share systems:A station-level analysis
6
作者 Zhuoli YIN Kendrick HARDAWAY +2 位作者 Yu FENG Zhaoyu KOU Hua CAI 《Frontiers of Engineering Management》 CSCD 2023年第4期551-565,共15页
Predicting demand for bike share systems(BSSs)is critical for both the management of an existing BSS and the planning for a new BSS.While researchers have mainly focused on improving prediction accuracy and analysing ... Predicting demand for bike share systems(BSSs)is critical for both the management of an existing BSS and the planning for a new BSS.While researchers have mainly focused on improving prediction accuracy and analysing demand-influencing factors,there are few studies examining the inherent randomness of stations'observed demands and to what degree the demands at individual stations are predictable.Using Divvy bike-share one-year data from Chicago,USA,we measured demand entropy and quantified the station-level predictability.Additionally,to verify that these predictability measures could represent the performance of prediction models,we implemented two commonly used demand prediction models to compare the empirical prediction accuracy with the calculated entropy and predictability.Furthermore,we explored how city-and system-specific temporallyconstant features would impact entropy and predictability to inform estimating these measures when historical demand data are unavailable.Our results show that entropy and predictability of demands across stations are polarized as some stations exhibit high uncertainty(a low predictability of 0.65)and others have almost no check-out demand uncertainty(a high predictability of around 1.0).We also validated that the entropy and predictability are a priori model-free indicators for prediction error,given a sequence of bike usage demands.Lastly,we identified that key factors contributing to station-level entropy and predictability include per capita income,spatial eccentricity,and the number of parking lots near the station.Findings from this study provide more fundamental understanding of BSS demand prediction,which can help decision makers and system operators anticipate diverse stationlevel prediction errors from their prediction models both for existing stations and for new ones. 展开更多
关键词 bike share systems demand prediction prediction errors machine learning ENTROPY
原文传递
Robust Charging Demand Prediction and Charging Network Planning for Heterogeneous Behavior of Electric Vehicles
7
作者 张轶伦 徐思坤 +3 位作者 徐捷 曾学奇 李铮 谢驰 《Journal of Shanghai Jiaotong university(Science)》 EI 2023年第1期136-149,共14页
This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the ... This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the heterogeneity of the charging behavior in a data-driven manner.To cope with the deficiencies from a small size and sparse behavioral data,we propose a robust charging demand prediction method that can significantly reduce the impact of sample errors and missing data.On the basis of these two building blocks,we form and solve a new optimal charging station location and capacity problem by minimizing the construction and charging costs while considering the charging service level,construction budget,and limit to the number of chargers.We use a case study of planning charging stations in Shanghai to validate our contributions and provide managerial insight in this area. 展开更多
关键词 electric vehicle charging network planning charging behavior robust demand prediction
原文传递
Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction 被引量:1
8
作者 Si Chen Yaxing Ren +2 位作者 Daniel Friedrich Zhibin Yu James Yu 《Energy and AI》 2020年第2期63-73,共11页
Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-qui... Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-quired for ANN training,data reduction and feature selection are important to simplify the training.However,in building heat demand prediction,many weather-related input variables contain duplicated features.This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features.The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus.The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20%training time compared with the traditional methods while maintaining the prediction accuracy.It indicates that the approach can be applied for analysing large num-ber of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications. 展开更多
关键词 Building heat demand prediction Statistical modelling Artificial neural network Sensitivity analysis Feature selection
原文传递
Urban construction land demand prediction and spatial pattern simulation under carbon peak and neutrality goals:A case study of Guangzhou,China
9
作者 HU Xintao LI Zhihui +1 位作者 CAI Yumei WU Feng 《Journal of Geographical Sciences》 SCIE CSCD 2022年第11期2251-2270,共20页
Urban construction land has relatively high human activity and high carbon emissions.Research on urban construction land prediction under carbon peak and neutrality goals(hereafter“dual carbon”goals)is important for... Urban construction land has relatively high human activity and high carbon emissions.Research on urban construction land prediction under carbon peak and neutrality goals(hereafter“dual carbon”goals)is important for territorial spatial planning.This study analyzed quantitative relationships between carbon emissions and urban construction land,and then modified the construction land demand prediction model.Thereafter,an integrated model for urban construction land demand prediction and spatial pattern simulation under“dual carbon”goals was developed,where urban construction land suitability was modified based on carbon source and sink capacity of different land-use types.Using Guangzhou as a case study,the integrated model was validated and applied to simulate the spatiotemporal dynamics of its urban construction land during 2030–2060 under baseline development and“dual carbon”goals scenarios.The simulation results showed that Guangzhou’s urban construction land expanded rapidly until 2030,with the spatial pattern not showing an intensive development trend.Guangzhou’s urban construction land expansion slowed during 2030–2060,with an average annual growth rate of 0.2%,and a centralized spatial pattern trend.Under the“dual carbon”goal scenario,Guangzhou’s urban construction land evolved into a polycentric development pattern in 2030.Compared with the baseline development scenario,urban construction land expansion in Guangzhou during 2030–2060 is slower,with an average annual growth rate of only 0.1%,and the polycentric development pattern of urban construction land was more prominent.Furthermore,land maintenance and growth,that is,a carbon sink,is more obvious under the“dual carbon”goals scenario,with the forest land area nearly 10.6%higher than that under the baseline development scenario.The study of urban construction land demand prediction and spatial pattern simulation under“dual carbon”goals provides a scientific decision-making support tool for territorial spatial planning,aiding in quantifying territorial spatial planning. 展开更多
关键词 carbon peak and neutrality goals urban construction land demand prediction spatial pattern simulation GUANGZHOU
原文传递
Prediction of the Logistics Demand Based on an Innovative Mixed Model: an Empirical Case from Nanping City,China 被引量:2
10
作者 王波 魏乐琴 +3 位作者 陈金雄 蔡尚斌 张立中 边舫 《Journal of Donghua University(English Edition)》 EI CAS 2019年第5期498-506,共9页
The research intends to make scientific prediction of the logistics demand of Nanping City based on mathematical model calculation so as to provide reasonable strategic guidance for the sustainable and healthy develop... The research intends to make scientific prediction of the logistics demand of Nanping City based on mathematical model calculation so as to provide reasonable strategic guidance for the sustainable and healthy development of urban logistics industry.It constructs a comprehensive index system composed of freight volume and other eight relevant economic indices to form the foundation for the model construction.Combining forecasting models of principal component regression and GM(1,1)together,it makes mathematical calculation to predict the logistics demand of Nanping City from the years 2018 to 2022.The research makes systematical analyses of the indices influencing the precise prediction of logistics demand from a new perspective,which offers an innovative and practical option for urban logistics prediction.In line with the prediction,it offers some suggestions for the improvement of demand prediction and some strategies for the better development of the logistics industry in Nanping City. 展开更多
关键词 regional LOGISTICS demand predictION principal component regression GM(1 1)prediction
下载PDF
Modeling and scenario prediction of a natural gas demand system based on a system dynamics method 被引量:6
11
作者 Xian-Zhong Mu Guo-Hao Li Guang-Wen Hu 《Petroleum Science》 SCIE CAS CSCD 2018年第4期912-924,共13页
Based on the study of the relationship between structure and feedback of China’s natural gas demand system, this paper establishes a system dynamics model. In order to simulate the total demand and consumption struct... Based on the study of the relationship between structure and feedback of China’s natural gas demand system, this paper establishes a system dynamics model. In order to simulate the total demand and consumption structure of natural gas in China, we set up seven scenarios by changing some of the parameters of the model. The results showed that the total demand of natural gas would increase steadily year by year and reach in the range from 3600 to 4500 billion cubic meters in 2035. Furthermore, in terms of consumption structure, urban gas consumption would still be the largest term, followed by the gas consumption as industrial fuel, gas power generation and natural gas chemical industry. In addition, compared with the population growth, economic development still plays a dominant role in the natural gas demand growth, the impact of urbanization on urban gas consumption is significant, and the promotion of natural gas utilization technology can effectively reduce the total consumption of natural gas. 展开更多
关键词 Natural gas demand system System dynamics Scenario prediction Consumption structure
下载PDF
Prediction of Commuter Vehicle Demand Torque Based on Historical Speed Information
12
作者 Shiji Sun Mingxin Kang Yuzhe Li 《Journal of Beijing Institute of Technology》 EI CAS 2022年第4期362-370,共9页
The development of vehicle-to-everything and cloud computing has brought new opportunities and challenges to the automobile industry.In this paper,a commuter vehicle demand torque prediction method based on historical... The development of vehicle-to-everything and cloud computing has brought new opportunities and challenges to the automobile industry.In this paper,a commuter vehicle demand torque prediction method based on historical vehicle speed information is proposed,which uses machine learning to predict and analyze vehicle demand torque.Firstly,the big data of vehicle driving is collected,and the driving data is cleaned and features extracted based on road information.Then,the vehicle longitudinal driving dynamics model is established.Next,the vehicle simulation simulator is established based on the longitudinal driving dynamics model of the vehicle,and the driving torque of the vehicle is obtained.Finally,the travel is divided into several accelerationcruise-deceleration road pairs for analysis,and the vehicle demand torque is predicted by BP neural network and Gaussian process regression. 展开更多
关键词 demand torque prediction commuter vehicle historical driving data machine learning
下载PDF
Prediction on the Farmland Demand of Yunnan Province in 2020 Based on Food Security
13
作者 YANG Long-fei1,ZHAO Qiao-gui1,2,YANG Zi-sheng1 1.Institute of Land & Resources and Sustainable Development,Yunnan University of Finance and Economics,Kunming 650221,China 2.Department of Land Resources of Yunnan Province,Kunming 650224,China 《Asian Agricultural Research》 2010年第3期58-61,共4页
According to the cultivated area and grain yield during 1996-2008 and adopting the prediction method of farmland demand based on food security,five indexes,including the cultivated area,grain sown area,yearly food yie... According to the cultivated area and grain yield during 1996-2008 and adopting the prediction method of farmland demand based on food security,five indexes,including the cultivated area,grain sown area,yearly food yield per unit area,total population and per capita grain yield,are selected to analyze and predict the farmland demand in Yunnan Province in 2020.As the prediction results of each index show,the total population of Yunnan Province in 2020 will reach 51 464 000,significantly higher than the upper bound(50 million);the per capita food demand of Yunnan Province in 2020 will be 400 kg below the bottom line of the well-off type;food self-sufficient ratio will be respectively given the value of 100%,95% and 90% in three schemes;the prediction will be conducted with the yearly food yield per unit area at an average annual growth rate of 2.5% and 3.0% in two schemes;the rate of grain sowing in 2010 is determined to be 66%.As the prediction results of farmland demand show,there are totally 6 schemes about farmland demand in Yunnan Province obtained through analysis,among them,scheme Ⅰ is difficult to achieve,the prediction results of scheme Ⅳ,Ⅴ and Ⅵ are relatively low,which do not conform to the state policies and regulations to protect farmland and are also not conductive for ensuring the food security;scheme Ⅱ and Ⅲ are close to each other,but scheme Ⅲ obtains better prediction results and determines the farmland demand of Yunnan Province in 2020 based on food security to be 5.9 million so as to ensure the provincial food security and realize the "red line" of basic provincial food self-sufficiency. 展开更多
关键词 Food security FARMLAND demand predictION YUNNAN Pr
下载PDF
Prediction on Cold Chain Logistics Demand of Urban Residents in Jiangsu Province during the Twelfth Five-Year Plan Period——Based on Estimates of GM(1,1) Model 被引量:2
14
作者 ZHENG Yan-min1,ZHANG Yan-cai2,XU Hong-feng2 1.School of Economics and Management,Nanjing University of Science & Technology,Nanjing 210094,China 2.School of Economics and Management,Huaiyin Normal University,Huaian 223001,China 《Asian Agricultural Research》 2011年第11期38-40,45,共4页
This paper takes the total yield of products that need refrigerated transport as the impact factors of transport aggregate of cold chain logistics,such as meat,aquatic products,quick-frozen noodle,fruits,vegetables,da... This paper takes the total yield of products that need refrigerated transport as the impact factors of transport aggregate of cold chain logistics,such as meat,aquatic products,quick-frozen noodle,fruits,vegetables,dairy,and medicine.Through selecting the consumption data of urban residents on transported products via cold chain in Jiangsu Province from 2005 to 2000 as sample,this paper establishes grey prediction model GM(1,1) of cold chain logistics demand and uses DPS7.05 software for test,to predict the cold chain logistics demand of urban residents in Jiangsu Province during the Twelfth Five-Year Plan period.The results show that in the period 2010-2015,the cold chain logistics demand of urban residents in Jiangsu Province is 1 151.589 1,1 185.136 6,1 219.661 3,1 255.191 8,1 291.757 3,1 329.388 1 t respectively;in the period 2005-2010,the cold chain logistics demand of urban residents in Jiangsu Province increases at annual growth rate of 3.9%;in the period 2011-2015,the growth rate declines to some extent,increasing slowly at rate of 2.9%. 展开更多
关键词 COLD CHAIN LOGISTICS demand The Twelfth Five-Year
下载PDF
基于主要驱动因子筛选法和深度学习算法的浙江省动态需水量预测
15
作者 许月萍 曾田力 +3 位作者 周欣磊 章鲁琪 王贝 王冬 《水利水电科技进展》 CSCD 北大核心 2024年第2期47-53,共7页
收集了浙江省2000—2020年各用水行业需水量数据,采用基于Spearman秩相关分析的主要驱动因子筛选法筛选了影响各行业需水量的主要驱动因子,进而构造了改进的长短时记忆(LSTM)神经网络需水量预测模型,对各行业需水量进行动态滚动预测,并... 收集了浙江省2000—2020年各用水行业需水量数据,采用基于Spearman秩相关分析的主要驱动因子筛选法筛选了影响各行业需水量的主要驱动因子,进而构造了改进的长短时记忆(LSTM)神经网络需水量预测模型,对各行业需水量进行动态滚动预测,并将改进LSTM模型的预测结果与传统单变量LSTM预测模型、卷积神经网络模型、支持向量回归模型的预测结果进行了对比。结果表明,基于主要驱动因子筛选法改进的LSTM模型能实时动态滚动预测各行业每年需水量,且预测结果精度高于其他3种模型。 展开更多
关键词 需水量预测 主要驱动因子筛选法 LSTM神经网络 卷积神经网络 支持向量回归 浙江省
下载PDF
面向动态交通分配的交通需求深度学习预测方法
16
作者 李岩 王泰州 +2 位作者 徐金华 陈姜会 汪帆 《交通运输系统工程与信息》 EI CSCD 北大核心 2024年第1期115-123,共9页
为满足动态交通分配对高精度、高时效性交通需求的要求,本文建立了一种交通需求深度学习预测方法。根据动态交通分配要求确定交通需求数据的时间间隔,构建对复杂交通需求预测性能较优的长短期记忆神经网络预测方法;针对动态交通分配中... 为满足动态交通分配对高精度、高时效性交通需求的要求,本文建立了一种交通需求深度学习预测方法。根据动态交通分配要求确定交通需求数据的时间间隔,构建对复杂交通需求预测性能较优的长短期记忆神经网络预测方法;针对动态交通分配中交通需求的周期性、随机性和非线性等特征,为减少数据噪声的干扰,引入局部加权回归周期趋势分解方法将交通需求数据分解,将其中的趋势分量和余项分量作为深度学习预测方法的输入量,周期分量采用周期估计进行预测;选用具有随机寻优能力强、寻优效率高等特点的布谷鸟寻优算法优化预测方法的隐藏层单元数量、学习速率和训练迭代次数等核心参数。应用西安市长安区的卡口车牌数据验证该方法。结果表明:本文模型的预测结果在高峰及平峰各连续4个时段内相比于自回归滑动平均模型、长短期记忆神经网络模型、支持向量回归模型,平均绝对误差降低了10.55%~19.80%,均方根误差降低了11.20%~17.99%,决定系数提升了8.62%~12.48%;相比遗传算法、粒子群算法优化的模型,平均绝对误差降低了7.36%~13.81%,均方根误差降低了4.23%~10.67%,决定系数提升了3.50%~7.01%,且本文模型运行时间最短。说明与对比模型相比,本文所建立的预测方法在面向动态交通分配的交通需求预测中具有更高的预测精度。 展开更多
关键词 智能交通 交通需求预测 布谷鸟寻优算法 长短期记忆神经网络 动态交通分配 局部加权回归周期趋势分解
下载PDF
中国城镇失能老年人口规模及养老服务需求预测 被引量:1
17
作者 程明梅 杨华磊 《北京社会科学》 北大核心 2024年第3期114-128,共15页
以全国第六次和第七次人口普查数据为基准数据,结合CLHLS微观数据库,对2050年以前城镇失能老年人口养老服务需求进行了预测。结果显示:随着年龄的增加,中国城镇老年人的失能率不断提高,其中在65岁以上、80岁以上及100岁以上的老年人群体... 以全国第六次和第七次人口普查数据为基准数据,结合CLHLS微观数据库,对2050年以前城镇失能老年人口养老服务需求进行了预测。结果显示:随着年龄的增加,中国城镇老年人的失能率不断提高,其中在65岁以上、80岁以上及100岁以上的老年人群体中,其平均失能率分别为28.98%、42.12%和76.04%;未来城镇重度失能老年人口规模将不断扩大,2050年以前其所占比例会超过城镇总失能老年人口的25%,而且男性重度失能人口规模始终低于女性重度失能人口规模;未来城镇重度失能老年人养老服务人员需求数量处于上升状态,预计2050年以前其每年需求的平均规模会超过500万人;随着城镇化的推进,未来城镇失能人口将高于农村失能人口。因此,应尽快建立覆盖城镇居民的长期照护机制;针对不同的城镇失能人群构建差异化的长期护理模式;加快建设针对城镇失能老年人的专业照护人员队伍;完善失能老年人养老服务规制。 展开更多
关键词 失能老年人 养老服务需求 人口预测 长期护理
下载PDF
A Predictive Nighttime Ventilation Algorithm to Reduce Energy Use and Peak Demand in an Office Building
18
作者 Hatef Aria Hashem Akbari 《Journal of Energy and Power Engineering》 2013年第10期1821-1830,共10页
关键词 夜间通风 节约能源 办公楼 算法 气候条件 室内外温差 模拟软件 建筑能耗
下载PDF
近红外光谱污水COD分析系统设计
19
作者 李阳 侯长宁 范日高 《福建电脑》 2024年第6期82-86,共5页
为了解决利用近红外光谱技术测定污水中的化学需氧量技术中样本数据集参数存在数据过拟合、干扰因素大、检测界面不直观等问题,本文采用一维卷积神经网络技术,建立水样近红外特征与污染指标成分含量之间的关系模型,并利用MATLAB的图形... 为了解决利用近红外光谱技术测定污水中的化学需氧量技术中样本数据集参数存在数据过拟合、干扰因素大、检测界面不直观等问题,本文采用一维卷积神经网络技术,建立水样近红外特征与污染指标成分含量之间的关系模型,并利用MATLAB的图形用户界面设计分析界面。实验结果表明,该方法预测结果良好,可实现水质污染的定性及定量的快速检测。 展开更多
关键词 污水 近红外光谱 化学需氧量预测
下载PDF
考虑风电出力不确定性的多源联合系统双层优化调度
20
作者 陈一鸣 刘赟静 王金鑫 《东北电力大学学报》 2024年第1期17-24,共8页
针对含风-火-储的多源联合系统,风电出力具有不确定性的特点,风机在特定时间段内的预测功率与实际功率之间存在误差,当风机实际出力无法满足调度计划中安排的功率时会导致系统经济效益大幅下降。为此,文中提出了考虑风电预测误差和需求... 针对含风-火-储的多源联合系统,风电出力具有不确定性的特点,风机在特定时间段内的预测功率与实际功率之间存在误差,当风机实际出力无法满足调度计划中安排的功率时会导致系统经济效益大幅下降。为此,文中提出了考虑风电预测误差和需求侧响应的双层优化策略,上层模型以风电、火电和可平移负荷总运行成本最少为目标,采用改进粒子群算法(Improved Particle Swarm Algorithm, IPSO)制定火电和可平移负荷的最优调度策略,然后通过Gibbs法对风机最大出力预测误差的概率密度函数进行抽样获取一定量的样本,得到各样本上层电源的功率缺额;下层模型以储能和可中断负荷总运行成本最少为目标,采用线性规划方法对冲上层电源功率缺额,进而制定下层模型电源调度策略。在大量抽样样本背景下,通过对比各样本总成本函数值的期望和方差验证了所提双层优化策略的经济性和有效性。 展开更多
关键词 风电预测误差 需求侧响应 IPSO 协同优化 GIBBS抽样
下载PDF
上一页 1 2 70 下一页 到第
使用帮助 返回顶部