检索结果-内蒙古大学图书馆

Multisource Heterogeneous Data Fusion Methods Driven by Digital Twin on Basis of prophet algorithm

IET SOFTWARE 2025年第1期2025卷

作者： Li, Min Jiangxi Univ Technol Sch Informat Engn Nanchang 330029 Jiangxi Peoples R China

With the development of intelligent manufacturing and the wider application of the Internet of Things (IoT), it is crucial to fuse heterogeneous sensor data from multiple sources. However, the current data fusion methods still have problems, such as low accuracy of fused data, insufficient data integrity, poor data fusion efficiency, and poor scalability of fusion methods. In response to these issues, this article explores a multisource heterogeneous data fusion method based on the prophet algorithm digital twin drive to improve the fusion effect of sensor data and provide more support for subsequent decision-making. The article first used curve and sequence alignment to extract data features and then analyzed the trend of data changes using the prophet algorithm. Afterward, this article constructed a digital twin model to provide analytical views and data services. In conclusion, this paper used tensor decomposition to merge text and image data from sensor data. Deep learning algorithms and Kalman filtering techniques were also examined to confirm the efficacy of data fusion under the prophet algorithm. The experimental results showed that after fusing the data using the prophet algorithm, the average accuracy can reach 92.63%, while the average resource utilization at this time was only 9.97%. The results showed that combining prophet with digital twin technology can achieve higher accuracy, fusion efficiency, and better scalability. The research in this paper can provide new ideas and means for the fusion and analysis of heterogeneous data from multiple sources.

关键词： data fusion methods digital twin feature extraction multisource heterogeneous data prophet algorithm

来源：评论

学校读者我要写书评

暂无评论

Error Prediction Method of Electronic Voltage Transformer based on Improved prophet algorithm

引用

RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2023年第5期16卷 551-559页

作者： Li, Zhenhua Zhong, Yue Abu-Siada, Ahmed Li, Qiu China Three Gorges Univ Hubei Prov Key Lab Operat & Control Cascaded Hyd Yichang 443002 Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China Curtin Univ Elect & Comp Engn Discipline Perth WA6012 Australia

Background: Electronic voltage transformer (EVT) is an essential metering device for future substation automation systems. One of the main drawbacks of EVT is its poor long-term stability, which affects its measurement accuracy. This will, in turn, adversely affect the entire protection and control systems it is employed for. Objective: Aiming at reducing the EVT measurement error over long-term operation, an EVT error prediction method combining prophet, temporal convolutional network (TCN) and self-attention is proposed in this paper. Methods: The proposed method is based on building prophet and TCN error prediction models to estimate preliminary prediction values. On this basis, self-attention is introduced to further extract features and make full use of the useful information in historical data. Then the secondary prediction can be achieved, and the final predicted value can be reported as an output. Results: The proposed method is validated by applying the error data of an EVT in a substation to its historical operation. The results show that the model can effectively predict the error trend of EVT. Conclusion: The prediction results of this method are similar to the fluctuations of the actual values, indicating that it provides a new reliable method for error prediction of EVT.

关键词： Electronic voltage transformer prophet algorithm TCN self-attention error prediction feature extraction

来源：评论

学校读者我要写书评

暂无评论

Occupational diseases forecasting for Polish coal mining based on prophet algorithm 27

Occupational diseases forecasting for Polish coal mining bas...

引用

27th International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Kashpruk, Nataliia Kraszewska, Marta Baranowski, Jerzy Kapusta, Mariusz AGH Univ Krakow Dept Automat Control & Robot Krakow Poland AGH Univ Krakow Fac Civil Engn & Resource Management Krakow Poland

ISBN: (纸本)9798350311075

In paper, forecasting models using prophet algorithm for occupational diseases incidence rate for Polish coal mining are presented. Prior to this, data is analyzed and approach for building forecasting models in prophet is described in details. Forecasting models for occupational diseases incidence rate are revealed, respectively for all sectors in Poland, mining industry and finally for coal mining including only pneumoconiosis. Improved forecast accuracy with presented models might provide coal mine enterprises more precise data, supporting safety management in those organizations.

关键词： occupational diseases pneumoconiosis forecasting numerical modeling time series coal mining prophet algorithm

来源：评论

学校读者我要写书评

暂无评论

Forecasting Research on Long-term Solar Irradiance with An Improved prophet algorithm 11th

Forecasting Research on Long-term Solar Irradiance with An I...

引用

11th IFAC Symposium on Control of Power and Energy Systems (CPES)

作者： Yang Xinpei Li Yiguo Shen Jiong Southeast Univ Natl Engn Res Ctr Power Generat Control & Safety Sch Energy & Environm Nanjing 210096 Peoples R China

To solve the problem of predicting solar irradiance in integrated energy systems (IES), this paper improves the prophet algorithm by removing the trend and holidays terms, changing to the multiplicative form, and adding the monthly regressors. The improved prophet algorithm is compared with the original and autoregressive integrated moving average (ARIMA) algorithms respectively, and the forecast accuracy is significantly improved, and the RMSE of the long-term forecast is 108.30% lower than that of ARIMA. The improved algorithm can be applied to forecast solar irradiance on long time scales with single information, providing a solution to the problem of PV array planning for IES. At the same time, the forecast data obtained by the algorithm can also be used as a reference value to optimize the day-ahead dispatch. Copyright (C) 2022 The Authors.

关键词： integrated energy systems (IES) solar irradiance Planning prophet algorithm Long-term forecasting forecasting

来源：评论

学校读者我要写书评

暂无评论

Using prophet algorithm for Pattern Recognition and Short Term Forecasting of Load Demand Based on Seasonality and Exogenous Features 52

Using Prophet Algorithm for Pattern Recognition and Short Te...

引用

52nd North American Power Symposium (NAPS)

作者： Parizad, A. Hatziadoniu, C. J. Southern Illinois Univ Elect & Comp Engn Dept Carbondale IL 62901 USA

ISBN: (纸本)9781728181929

As smart meters have proliferated in recent years, electrical power companies are dealing with a large volume of data, known as Big Data. Consistent with this issue, data science techniques are necessary to extract the patterns of consumption and forecast load at a specific area. Short-term load forecasting (STLF) models are crucial for electric utilities and play a vital role in generation planning, system operation, system security (false data detection), energy trading (buying/purchasing), reconfiguration, and contingency analysis. Nevertheless, electric consumption forecasting is challenging due to uncertainty incorporated in consumers' behavior. In this paper, we propose a comprehensive two-stage STLF method based on the prophet algorithm (PA). In the first stage and after data mining, load consumption patterns are extracted based on trend and seasonality features (e.g., spring, summer, autumn, winter, daily, weekly, etc.) along with holiday effects. Also, to make the model more robust, weather and lag data are added as an exogenous regressors. In the second step the mean absolute percentage error (MAPE) is defined as an objective function to find hyper parameters of the PA. The optimal hyper parameters maximize the accuracy of the fitted model and result in an appropriate prediction based on the historical data. To show the performance of the proposed method Commonwealth Edison Company (ComEd., Northern Illinois, PJM RTO) data set including load and weather data is used. Three case studies are investigated to predict day-ahead load profiles employing different PA hyper parameters. The Simulation results developed in Python programing language reveal that different load patterns can be extracted by this method. Furthermore, different hyper setting results in distinct forecasting while one case can trace light load hours, the other one can follow peak load hours, more accurately. For all of the case studies, the next 24-hour load can be forecasted within an acce

关键词： Load pattern recognition Short-term load forecasting Model training hyper parameter optimization Seasonality exogenous features prophet algorithm

来源：评论

学校读者我要写书评

暂无评论

Trend Analysis of Building Power Consumption Based on prophet algorithm

Trend Analysis of Building Power Consumption Based on Prophe...

引用

2020 Asia Energy and Electrical Engineering Symposium

作者： Gong, Feixiang Han, Ninghui Li, Dezhi Tian, Shiming China Elect Power Res Inst Co Ltd Inst Power Consumpt & Energy Efficiency Beijing Peoples R China

ISBN: (纸本)9781728167824

There are many influencing factors of building electricity consumption, such as weather, season, time, economy and electricity price, which have the characteristics of certain linear and periodic changes, and have strong random changes. In this paper a novel prophet algorithm was proposed to do trend analysis of building power consumption. By comparing the prediction results of prophet algorithm and ARIMA algorithm, prophet algorithm has better prediction results in terms of prediction and can add the date and time that affect the building. The simulation results show that the comprehensive power consumption of shopping malls is affected by holidays to some extent, but also by high temperature weather to some extent. The comprehensive power consumption of office buildings is relatively less affected by holidays and high temperature weather to some extent.

关键词： prophet algorithm power consumption trend analysis building

来源：评论

学校读者我要写书评

暂无评论

Trend Analysis of Building Power Consumption Based on prophet algorithm

Trend Analysis of Building Power Consumption Based on Prophe...

引用

IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Feixiang Gong Ninghui Han Dezhi Li Shiming Tian China Electric Power Research Institute Co. Ltd Institute of Power Consumption and Energy Efficiency Beijing China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

关键词： prophet algorithm power consumption trend analysis building trend analysis Power consumption Buildings Holidays electricity consumption shopping mall algorithms office buildings

来源：评论

学校读者我要写书评

暂无评论

Solar Energy Forecasting Framework Using prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman

引用

ENERGIES 2025年第1期18卷 205-205页

作者： Baloch, Mazhar Honnurvali, Mohamed Shaik Kabbani, Adnan Ahmed, Touqeer Chauhdary, Sohaib Tahir Saeed, Muhammad Salman ASharqiyah Univ Coll Engn Dept Elect Engn & Comp Sci Ibra 400 Oman Muscat Univ Fac Engn & Technol Muscat 113 Oman Dhofar Univ Coll Engn Dept Elect & Comp Engn Salalah 201 Oman Multan Elect Power Co Multan 60000 Punjab Pakistan

The unpredictable nature of renewable energy sources, such as wind and solar, makes them unreliable sources of energy for the power system. Nevertheless, with the advancement in the field of artificial intelligence (AI), one can predict the availability of solar and wind energy in the short, medium, and long term with fairly high accuracy. As such, this research work aims to develop a machine-learning-based framework for forecasting global horizontal irradiance (GHI) for Muscat, Oman. The proposed framework includes a data preprocessing stage, where the missing entries in the acquired data are imputed using the mean value imputation method. Afterward, data scaling is carried out to avoid the overfitting/underfitting of the model. Features such as the GHI cloudy sky index, the GHI clear sky index, global normal irradiance (GNI) for a cloudy sky, GNI for a clear sky, direct normal irradiance (DNI) for a cloudy sky, and DNI for a clear sky are extracted. After analyzing the correlation between the abovementioned features, model training and the testing procedure are initiated. In this research, different models, named Linear Regression (LR), Support Vector Machine (SVR), KNN Regressor, Decision Forest Regressor, XGBoost Regressor, Neural Network (NN), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor, Categorical Boosting (CatBoost), Deep Autoregressive (DeepAR), and Facebook prophet, are trained and tested under both identical features and a training-testing ratio. The model evaluation metrics used in this study include the mean absolute error (MAE), the root mean squared error (RMSE), R2, and mean bias deviation (MBD). Based on the outcomes of this study, it is concluded that the Facebook prophet model outperforms all of the other utilized conventional machine learning models, with MAE, RMSE, and R2 values of 9.876, 18.762, and 0.991 for the cloudy conditions and 11.613, 19.951 and 0.988 for the clean weather con

关键词： solar energy forecasting prophet algorithm machine learning framework prophet ML model

来源：评论

学校读者我要写书评

暂无评论

Bidirectional LSTM for Air Quality Forecast

Bidirectional LSTM for Air Quality Forecast

引用

Intelligent Systems Conference

作者： Olaifa, Moses Zuva, Tranos Vaal Univ Technol Fac Appl & Comp Sci Dept Informat & Commun Technol Vanderbiljpark South Africa

ISBN: (纸本)9783031664304;9783031664311

An increase in the number of industries and other human activities have raised the level of contamination of surrounding air. It is imperative to build good prediction models with high accuracy in air quality forecast. In spite of the efforts in research, identifying suitable models with high accuracy is still a challenge especially for locations with less advanced monitoring stations using low cost sensors. Data collected in such situation are prone to missing data in large intervals and the possibility of anomalies is high. This study proposes a bidirectional LSTM (BiLSTM) for the forecast of fine particulate matter pollutant in the air. The performance of a stacked BiLSTM based on the Relu function is compared with two other revised models. The proposed model presented a better performance than the other two models in cases of better learning dependencies and characteristics of the data.

关键词： Smooth particulate matter LSTM Bidirectional LSTM prophet algorithm

来源：评论

学校读者我要写书评

暂无评论

Forecasting Demand for Donuts Using a Machine Learning Approach 8

Forecasting Demand for Donuts Using a Machine Learning Appro...

引用

8th International Conference on Electrical, Telecommunication and Computer Engineering, ELTICOM 2024

作者： Sembiring, Nurhayati Sari Harahap, Wanda Burma Khalif, Farhan Abiyyahda Chandra, Karina Herlim, Christian Universitas Sumatera Utara Department of Industrial Engineering Medan Indonesia

ISBN: (纸本)9798331531249

UKM Donat XYZ is a company focused on producing donuts with a daily capacity of 4,000 to 5,000 units, offering more than 20 flavor variants. However, the company experiences monthly sales fluctuations and often encounters issues with unsold donut stock due to difficulties in accurately predicting production volumes manually. The research was conducted to forecast donut demand in 2024 using a machine-learning approach to address this challenge. The study leveraged historical daily sales data from 2022 and 2023, totaling 730 data points. Two methods were employed: the Naïve-Bayes algorithm and prophet. Evaluation of the results indicated that the Naïve- Bayes algorithm achieved better accuracy than prophet, with a Mean Square Error (MSE) of 428,328.92 for Naïve-Bayes. At the same time, prophet had an MSE of 27,178,280. This research provides valuable insights for the company to efficiently manage its production and optimize donut stock. By utilizing predictive models like Naïve-Bayes, the company can better forecast market demand and avoid situations of potential stock shortages or excesses that could lead to losses. It also aids in more accurate production planning and efficient resource management. This research is anticipated to serve as a foundation for the company to enhance its strategies in meeting consumer demand more timely and effectively. Consequently, UKM Donat XYZ can improve its operational performance and reduce potential losses from wasted products due to errors in demand estimation. © 2024 IEEE.

关键词： Demand Forecasting Machine Learning Naïve-Bayes algorithm prophet 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：