An Offshore Equipment Data Forecasting System

2019 ◽  
pp. 115-126
Author(s):  
Ahmad Syazwan Sahdom ◽  
Alan Cheah Kah Hoe ◽  
Jaspaljeet Singh Dhillon
Keyword(s):  
Forecasting System

Development and Operational Status of Wind Power Forecasting System

2013 ◽  
Vol 133 (4) ◽  
pp. 366-372 ◽  
Author(s):  
Isao Aoki ◽  
Ryoichi Tanikawa ◽  
Nobuyuki Hayasaki ◽  
Mitsuhiro Matsumoto ◽  
Shigero Enomoto
Keyword(s):  
Wind Power ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

Implementasi Metode Winter untuk Forecasting Pertumbuhan Jumlah Penduduk: Studi Kasus Wilayah Provinsi NTB

2020 ◽  
Vol 3 (1) ◽  
pp. 51-61
Author(s):  
Syaharuddin ◽  
Abdul Adhiim Rizky ◽  
Lutfi Jauhari ◽  
Siti Fatimah ◽  
Wahyu Ningsih ◽  
...  
Keyword(s):  
Time Series Data ◽  
Optimal Parameter ◽  
Series Data ◽  
Percentage Error ◽  
Average Error ◽  
Ratio Data ◽  
Female Data ◽  
Forecasting System ◽  
Conducting Research ◽  
And Gender

This research aims to analyse the acceleration of population growth based on gender in West Nusa Tenggara Province (NTB) using the Forecasting system by constructing the winter's method in the shape of the Multiple Forecasting System (G-MFS) based on Matlab by calculating the period indicator for accuracy to find time series data in the year 2020-2029. At the simulation stage, researchers used the population and gender ratio data in NTB Province in 2009-2019. The method used in conducting research is to use the winter's method. The evaluation of Forecasting results is done by calculating the average error value using the Mean Absolute Percentage Error (MAPE) method. From this study obtained the most optimal parameter value on male data namely ʌ, β and γ sequential values of 0.9, 0.5 and 0.9 while in female data, the value of ʌ, β and γ respectively, 0.2, 0.1 and 0.5. Then with the value of the parameter obtained MAPE value in male data of 1.7785% and in female data of 0.89034%.

Air quality forecasting system based on chemical transport models

2019 ◽  
Vol 4 ◽  
pp. 203-218
Author(s):  
I.N. Kusnetsova ◽  
◽  
I.U. Shalygina ◽  
M.I. Nahaev ◽  
U.V. Tkacheva ◽  
...  
Keyword(s):  
Air Quality ◽  
Chemical Transport ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Forecasting System ◽  
Air Quality Forecasting

Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

2003 ◽  
Author(s):  
Hans C. Graber ◽  
Mark A. Donelan ◽  
Michael G. Brown ◽  
Donald N. Slinn ◽  
Scott C. Hagen ◽  
...  
Keyword(s):  
Real Time ◽  
Tropical Cyclones ◽  
Forecasting System

Catastrophic Effects of Floods on Environment and Health: Evidence from Pakistan

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Falak Shad Memon ◽  
M. Yousuf Sharjeel
Keyword(s):  
Paradigm Shift ◽  
Weather Forecasting ◽  
Human Life ◽  
National Level ◽  
Operational Cost ◽  
Site Assessment ◽  
Environment And Health ◽  
Forecasting System ◽  
Frame Work ◽  
Multiple Levels

<span>Torrential rains and floods have been causing irreplaceable losses to both human lives and environment in <span>Pakistan. This loss has reached to an extent of assively aggrieved situation to reinstate life at <span>operationally viable position. This paper unfolds the notion that only constructive paradigm shift to <span>overcome this phenomenon is vital as a strategy. Multiple levels of observations and on-site assessment <span>of various calamity-prone venues were considered to probe into this scenario. Some of the grave site in <span>Sindh and Punjab were observed and necessarily practicable measures were recommended to avoid loss to <span>human health and environment. The paper finds that a consistent drastic management authority on <span>national level with appropriate caliber and forecasting expertise can reduce the damage to human life and <span>environment to great extent. Weather forecasting system need to be installed at many appropriately <span>observed cities and towns in the country with adequate man power, funds and technical recourses. By <span>implementing the proper frame work of prevention and mitigation of floods country can save the major <span>costs cleanup and recovery. These measures are expected to reduce operational cost of state in terms of <span>GDP and GNP to restore life and environment.</span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>

A novel interval forecasting system for uncertainty modeling based on multi-input multi-output theory: A case study on modern wind stations

2021 ◽  
Vol 163 ◽  
pp. 88-104
Author(s):  
Tongxiang Liu ◽  
Qiujun Zhao ◽  
Jianzhou Wang ◽  
Yuyang Gao
Keyword(s):  
Uncertainty Modeling ◽  
Forecasting System

scholarly journals C‐LAEF : Convection‐permitting Limited‐Area Ensemble Forecasting system

2021 ◽  
Author(s):  
Clemens Wastl ◽  
Yong Wang ◽  
Aitor Atencia ◽  
Florian Weidle ◽  
Christoph Wittmann ◽  
...  
Keyword(s):  
Ensemble Forecasting ◽  
Limited Area ◽  
Forecasting System

GPFS: A Graph-based Human Pose Forecasting System for Smart Home with Online Learning

2021 ◽  
Vol 17 (3) ◽  
pp. 1-19
Author(s):  
Xin Li ◽  
Dawei Li
Keyword(s):  
Online Learning ◽  
Smart Home ◽  
Data Privacy ◽  
Structural Information ◽  
Learning Technology ◽  
Extensive Evaluation ◽  
Forecasting System ◽  
Human Pose ◽  
Human Joints ◽  
Visual Domain

Forecasting human poses given a sequence of historical pose frames has several important applications, especially in the domain of smart home safety. Recently, computer vision-based human pose forecasting has made a breakthrough using deep learning technology. However, to implement a practical system deployed on an IoT edge environment, there are still two issues to be addressed. First, existing methods on pose forecasting fail to model the coherent structural information of connected human joints and thus cannot achieve satisfactory prediction accuracy, especially for long-term predictions. Second, a general and static pre-trained prediction model may not perform well in the deployment environment due to the visual domain shift problem. In this article, we propose a hybrid cloud-edge system called GPFS to solve those issues. Specifically, we first introduce a novel graph convolutional neural network (GCN)-based sequence-to-sequence learning method, which enhances the sequence encoder by using a graph to represent both the spatial and temporal connections of the human joints in the input frames. The GCN improves the forecasting accuracy by capturing the motion pattern of each joint as well as the correlations among different human joints over time. Second, to address the domain shift issue and protect data privacy, we extend the system to perform online learning on the IoT edge to adapt the cloud trained general model with online collected on-site domain data. Extensive evaluation on Human 3.6M and Penn Action datasets demonstrates the superiority of our proposed system.

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