检索结果-内蒙古大学图书馆

International Conference on Civil, Architectural and Hydraulic Engineering (ICCAHE 2012)

作者： Fang, Tingyong Chen, Jun Fang, Meixiang Wang, Jing Minist Educ Engn Res Ctr Bldg Energy Efficient Control & Eval Hefei 230022 Anhui Peoples R China

ISBN: (纸本)9783037854853

Nowadays the researches of building energy efficiency mainly focus on the development of the related technologies and management measures, but the regional policy EIA have a greater impact on the development of building energy efficiency in China. In this paper, the statistical data combined with the actual situation of building energy efficiency during the period of the Eleventh-Five-Year in Anhui Province, China. Use exponential smoothing method of time series algorithm for forecast and analysis of building energy consumption and energy saving in Anhui Province. First, take six different values as the exponential smoothing constants, the predictive values are calculated under the different exponential smoothing constants. Then compare and analyze the curves through the errors between the predictive values and the actual values, and determine the group of the curves of which the trend closest to the trend of actual values. For the actual data are affected by many kinds of factors that exist fluctuations, the method for predictive values correction was put forward. Using the trend extrapolation, the predictive values is corrected. Also provides theoretical guidance for the future work in Anhui Province and other similar places.

关键词： time series algorithm Building energy efficiency Exponential smoothing Energy consumption Energy saving

来源：评论

学校读者我要写书评

暂无评论

Vendor-to-Retailer Vegetables and Fruits Ordering Application Using time series algorithm 2

Vendor-to-Retailer Vegetables and Fruits Ordering Applicatio...

引用

2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Sathya, T. Prabhu, D. Dhanush, Prabhu K Harithaa, S. Luckysha, M. Venkat, Ram S Sri Sairam Engineering College Computer Science & Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798350308846

In this paper, we propose a vendor-to-retailers vegetable and fruit ordering application that streamlines the supply chain process. The application provides a platform for vendors and retailers to interact and conduct business transactions in a seamless and efficient manner. Vendors can use the application to upload their available inventory, pricing, and delivery schedules, while retailers can use the application to place orders, track deliveries, and make payments. The application is built on a user-friendly interface that allows vendors and retailers to easily navigate and manage their accounts. The system also employs a time series algorithm to predict future demand and optimize the allocation of vegetables and fruits to different retailers. The algorithm uses historical sales data and other relevant factors, such as seasonality and weather patterns, to provide accurate and timely forecasts. This can help vendors to make informed decisions about inventory management, production planning, and pricing strategies. It also improves the efficiency and enhance customer satisfaction by ensuring that fresh produce is available when and where it is needed. © 2023 IEEE.

关键词： demand forecasting inventory management supply chain time series algorithm vegetable and fruit ordering application Vendor-to-retailer

来源：评论

学校读者我要写书评

暂无评论

Sunspot Activity and Cycle Prediction Study Based on time series algorithm and CNN-LSTM Modeling 4

Sunspot Activity and Cycle Prediction Study Based on Time Se...

引用

4th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Peng, Xinye University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798350359589

This study investigates the relationship between sunspot activity and solar cycle variations, utilizing time series algorithms and the CNN-LSTM model for prediction. Initial preprocessing of sunspot number and area data involved outlier elimination and linear interpolation. The ARIMA time series algorithm was chosen for its ability to integrate convolutional neural networks and long short-term memory networks, enhancing prediction accuracy. The model forecasts the current solar cycle from December 2019 to February 2030 and the next from February 2030 to June 2040. Additionally, the study predicts the onset and duration of the next solar maximum and calculates sunspot numbers and areas for the current and upcoming cycles. The research effectively combines temporal and spatial data analysis, offering a comprehensive approach to predicting solar cycle dynamics. © 2024 IEEE.

关键词： CNN-LSTM Model Solar Cycle Prediction Sunspot Activity time series algorithm

来源：评论

学校读者我要写书评

暂无评论

Application of Fuzzy Control Based on time series Prediction algorithm in Main Steam Temperature System

Application of Fuzzy Control Based on Time Series Prediction...

引用

Chinese Automation Congress (CAC)

作者： Lv Zhengxin Zhang Yue North China Elect Power Univ Engn Res Ctr Power Generat Simulat & Optimizat Co Baoding Baoding Peoples R China

ISBN: (纸本)9781728113128

The stability of main steam temperature in thermal power plants is especially important for boiler operation. Conventional PID has poor control effect on large delay targets of main steam temperature system in thermal power plants. It is difficult to achieve satisfactory control results. In view of this situation, a PID-based fuzzy controller based on the time series prediction algorithm was designed The time series algorithm can predict the main steam temperature at the next moment and calculate the input value of the PID fuzzy controller according to the predicted value. The fuzzy control that has the leading characteristic and the regulator advances to reduce the overshoot and adjustment time obviously. The BP neural network algorithm is used to correct the prediction results of the time series algorithm, which makes the algorithm more stable and safe The simulation and experimental results show that the control effect is significantly better, indicating that this is an effective improvement method, which is obviously superior to the traditional PID control and PID-type fuzzy control.

关键词： PID fuzzy control time series algorithm main steam temperature system BP neural network

来源：评论

学校读者我要写书评

暂无评论

Big data-assisted urban governance: forecasting social events with a periodicity by employing different time series algorithms

引用

LIBRARY HI TECH 2023年第6期42卷 1930-1955页

作者： Zhang, Zicheng Lin, Xinyue Shan, Shaonan Yin, Zhaokai Nanjing Univ Sch Informat Management Nanjing Peoples R China Shenyang Univ Shenyang Peoples R China

Purpose - This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social ***/methodology/approach - In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public *** - The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable ***/value - The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.

关键词： Government hotline Urban governance Public event time series algorithm Periodic social problem

来源：评论

学校读者我要写书评

暂无评论

Study of time series algorithms for real-time weather prediction for a Gamma-ray Experiment 30

Study of time series algorithms for real-time weather predic...

引用

30th International Conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Sarkar, Debangana Chouhan, Nilesh Yadav, Kuldeep Homi Bhabha Natl Inst Bhabha Atom Res Ctr Mumbai Maharashtra India Bhabha Atom Res Ctr Astrophys Sci Div Mumbai Maharashtra India

ISBN: (纸本)9798350383782;9798350383799

Weather parameters and weather forecasting play a crucial role in scientific experiments for the safe operation of the system. Weather prediction involves high-performance computing, which enables building of weather models. Major Atmospheric Cherenkov Experiment (MACE) is a 21m ground-based, very high-energy gamma-ray telescope installed at Hanle, India. Accurate and reliable short-term weather prediction plays a crucial role in ensuring the safe and efficient functioning of the telescope. In the first part of the paper, we describe the multithreaded design of the real-time weather data capture application for the telescope. In the second part, we compare the performance of time series algorithms for weather prediction and the possibility of hybrid models for predicting non-stationary weather factors.

关键词： Gamma Ray Experiment Weather Data Analysis time series algorithm Computation

来源：评论

学校读者我要写书评

暂无评论

Designing solar power generation output forecasting methods using time series algorithms

引用

ELECTRIC POWER SYSTEMS RESEARCH 2023年 216卷

作者： Kim, EunGyeong Akhtar, M. Shaheer Yang, O-Bong Jeonbuk Natl Univ Grad Sch Integrated Energy AI Jeonju 54896 South Korea Jeonbuk Natl Univ Sch Semicond & Chem Engn Jeonju 54896 South Korea

The present photovoltaic (PV) power generation systems are globally facing the irregularity problem in the distribution of PV generation. In particular, the exact PV power forecasting is critical for grid-connected photovoltaic (PV) systems under unwanted changes in environmental circumstances. The grid energy manage-ment, grid operation and scheduling are important factors to forecast the PV power output. time series analysis is one of the most important aspects of PV output prediction, especially in places (in South Korea) where past solar radiation data or other weather parameters have not been recorded. In this paper, a variety of time-series methods including deep-learning algorithm and machine learning algorithms was used to predict the PV power generation output for quick respond to equipment and panel defects. For designing AI models, the input data were characterized by dividing seasons and choosing the multiple parameters from seasons. In this study, the photovoltaic power generation data was collected from Ansan city, South Korea during January 2017 to June 2021 and the weather data was collected from Suwon city, South Korea during January 2017 to June 2021. In this work, approx. 40,000 hours of operation data from 1.5 MW grid-connected PV system in South Korea was used. PV power generation forecasting was carried out on an hourly basis to test efficacy of various models. Among all models (Holt-Winters, Multivariate Linear Regression, ARIMA, SARIMA, ARIMAX, SARIMAX), LSTM model presented the lowest error rate as compared to other models for quick PV power generation forecasting.

关键词： Photovoltaic power output Forecasting methods Machine learning Deep learning Climate change time series algorithm

来源：评论

学校读者我要写书评

暂无评论

The physical series algorithm of mid-long term load forecasting of power systems

引用

ELECTRIC POWER SYSTEMS RESEARCH 2000年第1期53卷 31-37页

作者： Xie, D Yu, JY Yu, JL Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200030 Peoples R China Shanghai Power Ind Bur Sch Elect Engn Shanghai 200240 Peoples R China Haerbin Inst Technol Dept Elect Engn Haerbin 150001 Peoples R China

The fluctuation of load of power systems is influenced by many factors. Mid-long term load forecasting is not only based on the information and materials of the power system itself, but is also limited by those of the social and economic as well as other factors of the area. Taking into consideration the influences of non-power systems, this paper has classified the different types of loads and proposed the basic conception of a 'load norm' of power systems and the physical series algorithm, and finally set up the mathematical models for the relevant mid-long term load forecasting. This paper has used this method to forecast the total consumption of the power load for a province in East China and that of the heavy industry of another province in Northwest China. The results proved to have a high precision of inverse calculation and forecasting credibility. (C) 2000 Elsevier Science S.A. All rights reserved.

关键词： power systems mid-long term load forecasting physical series algorithm load norm time series algorithm

来源：评论

学校读者我要写书评

暂无评论

Rumor Detection Based on Attention CNN and time series of Context Information

引用

FUTURE INTERNET 2021年第11期13卷 267页

作者： Peng, Yun Wang, Jianmei Jiangxi Normal Univ Sch Comp Informat & Engn Nanchang 330022 Jiangxi Peoples R China

This study aims to explore the time series context and sentiment polarity features of rumors' life cycles, and how to use them to optimize the CNN model parameters and improve the classification effect. The proposed model is a convolutional neural network embedded with an attention mechanism of sentiment polarity and time series information. Firstly, the whole life cycle of rumors is divided into 20 groups by the time series algorithm and each group of texts is trained by Doc2Vec to obtain the text vector. Secondly, the SVM algorithm is used to obtain the sentiment polarity features of each group. Lastly, the CNN model with the spatial attention mechanism is used to obtain the rumors' classification. The experiment results show that the proposed model introduced with features of time series and sentiment polarity is very effective for rumor detection, and can greatly reduce the number of iterations for model training as well. The accuracy, precision, recall and F1 of the attention CNN are better than the latest benchmark model.

关键词： rumor event detection sentiment polarity time series algorithm attention CNN

来源：评论

学校读者我要写书评

暂无评论

Predictive Data Mining and Pattern Recognition in the Medical Sector: Implementation and Experience

Predictive Data Mining and Pattern Recognition in the Medica...

引用

World Congress on Computer Applications and Information Systems (WCCAIS)

作者： Shabbir, Aqsa Ansari, Yasmin Tauqeer Kazim, Ali Hussain El-Hassan, Ammar Women Univ Lahore Coll Dept Elect & Elect Engn Lahore Pakistan Prince Muhammad bin Fahd Univ Dept Comp Engn & Sci Al Khobar Saudi Arabia Univ Engn & Technol Dept Mechna Engn Lahore Pakistan

ISBN: (纸本)9781479933518

Large enterprises essentially require a reliable infrastructure that can store, retrieve and analyze the massive volumes of high dimensional data. Knowledge Discovery in Databases (KDD) through Data Mining (OM) presents a powerful tool for storing and retrieving data in a manner that optimizes performance as well as resources. This work presents the application of time series Data Mining algorithm to Hospital Management Information System (HMIS) in a public sector hospital in Pakistan. Public sector hospitals in Pakistan, apart from being typically overcrowded are severely limited by lack of resources. This research focuses on the cost effective application of KDD alongside correctly predicting disease patterns, hospital admittance rate and patient turn out. The results of this work not only bring about an improvement in management for this hospital but also provide a model for other health care facilities in the developing world.

关键词： Hospital Management Information System Knowledge Discovery in Databases Data Mining decision making time series algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：