scholarly journals Application of Facebook’s Prophet Model for Forecasting Meteorological Data

2021 ◽  
Vol 21 (2) ◽  
pp. 53-58
Author(s):  
Junsuk Kim ◽  
Tae Jin Kim
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Meteorological Data ◽  
Risk Index ◽  
Basic Research ◽  
Time Series Forecasting ◽  
Spreading Speed ◽  
Future Research ◽  
Fire Occurrence ◽  
Computing Speed

The wildfire risk index was calculated based on current meteorological information, for example, temperature, humidity, and wind speed. Thus, meteorological data forecasting could help estimate the probability of fire occurrence or spreading speed to prevent large wildfires. This study predicts meteorological data (e.g., temperature, humidity, and wind speed) using Facebook's Prophet library. We trained the Prophet model using meteorological data between 2016 and 2018 in Goseong, Gangwon-do (where the wildfire occurred in 2019) and predicted meteorological data for the first four months in 2019. We obtained that Facebook's Prophet model was effective in computing speed and predicting the overall trend. However, it could not predict sudden irregular changes satisfactorily. Considering its rapidity, these results could play an important role in future research, especially as a basic research for time-series forecasting.

scholarly journals ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2578 ◽  
Author(s):  
Neeraj Dhanraj Bokde ◽  
Zaher Mundher Yaseen ◽  
Gorm Bruun Andersen
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Solar Radiation ◽  
Test Bench ◽  
Performance Metrics ◽  
R Package ◽  
Time Series Forecasting ◽  
Comparative Performance ◽  
Forecasting Methods ◽  
Natural Time

This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset. The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. The proposed test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided to visualize the comparative performance and accuracy of different forecasting methods. Furthermore, this paper presents real application examples with natural time series datasets (i.e., wind speed and solar radiation) to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset. Modeling results indicated the applicability and robustness of the proposed R package ForecastTB for time series forecasting.

scholarly journals Wind speed time series forecasting method in wind farm based on EEMD

2021 ◽  
Vol 791 (1) ◽  
pp. 012140
Author(s):  
Yu Dongyang ◽  
Sun Fengchang ◽  
Deng Xiaochuan ◽  
Wang Zheng ◽  
Wu Jiahua ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Farm ◽  
Time Series Forecasting ◽  
Forecasting Method

scholarly journals Simultaneous Forecasting of Meteorological Data Based on a Self-Organizing Incremental Neural Network

2018 ◽  
Vol 22 (6) ◽  
pp. 900-906 ◽  
Author(s):  
Wonjik Kim ◽  
Osamu Hasegawa ◽  
◽  
◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Atmospheric Pressure ◽  
Time Series Data ◽  
Meteorological Data ◽  
Series Data ◽  
Total Solar Radiation ◽  
Proposed Model ◽  
Self Organizing

In this study, we propose a simultaneous forecasting model for meteorological time-series data based on a self-organizing incremental neural network (SOINN). Meteorological parameters (i.e., temperature, wet bulb temperature, humidity, wind speed, atmospheric pressure, and total solar radiation on a horizontal surface) are considered as input data for the prediction of meteorological time-series information. Based on a SOINN within normalized-refined-meteorological data, proposed model succeeded forecasting temperature, humidity, wind speed and atmospheric pressure simultaneously. In addition, proposed model does not take more than 2 s in training half-year period and 15 s in testing half-year period. This paper also elucidates the SOINN and the algorithm of the learning process. The effectiveness of our model is established by comparison of our results with experimental results and with results obtained by another model. Three advantages of our model are also described. The obtained information can be effective in applications based on neural networks, and the proposed model for handling meteorological phenomena may be helpful for other studies worldwide including energy management system.

scholarly journals The Impact of Heat Change Intensity on the Prevalence of Work-Related Accidents in Isfahan Steel Industry; A Time-Series Approach

2020 ◽  
Author(s):  
Hadi Alimoradi ◽  
Mahsa Nazari ◽  
Mohammad Javad Zare Sakhvidi
Keyword(s):  
Time Series ◽  
Heat Stress ◽  
Wind Speed ◽  
Steel Industry ◽  
Thermal Stresses ◽  
Meteorological Data ◽  
Daily Basis ◽  
Work Related ◽  
Change Intensity ◽  
The Impact

In the steel industries, workers are exposed to heat and ambient thermal stresses on a daily basis, leading to discomfort and limited performance. In this study, the main purpose is to investigate the effect of climate heat stress on the rate of accidents in the workplace for workers for 5 consecutive years. The data of this study were received without any sampling through the HSE Center for Steel Industry and meteorological data from 2015 to 2019 from Isfahan Meteorological station. The daily number of casualties among workers in the steel industry during 2015-2019 by adjusting seasonal patterns, months, effects of the day of the week and other meteorological factors on the average daily temperature using the studied model has a decreasing effect. Eviews software (version 8) was used to model and investigate the relationship between events and meteorological variables. The mean temperature was at least 40.2-2 and at most 70.34 ° C, respectively. In the time-series study for the main model, the number of accidents shows a direct relationship with the average temperature and wind speed. Climatic indices of humidity and rainfall have the least impact on accidents compared to temperature and wind speed. A strong correlation was shown between the increase in average ambient temperature and the rate of accidents over the past 5 years. Given the fundamental differences in studies of environmental exposure and wind speed over heat stress, further analysis in workers should be considered.

Applications of Recurrent Neural Networks in Environmental Factor Forecasting: A Review

2018 ◽  
Vol 30 (11) ◽  
pp. 2855-2881 ◽  
Author(s):  
Yingyi Chen ◽  
Qianqian Cheng ◽  
Yanjun Cheng ◽  
Hao Yang ◽  
Huihui Yu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Environmental Factor ◽  
Recurrent Neural Networks ◽  
Feedforward Neural Networks ◽  
Time Series Forecasting ◽  
Learning Ability ◽  
Future Research ◽  
Sequential Data ◽  
Novel Applications

Analysis and forecasting of sequential data, key problems in various domains of engineering and science, have attracted the attention of many researchers from different communities. When predicting the future probability of events using time series, recurrent neural networks (RNNs) are an effective tool that have the learning ability of feedforward neural networks and expand their expression ability using dynamic equations. Moreover, RNNs are able to model several computational structures. Researchers have developed various RNNs with different architectures and topologies. To summarize the work of RNNs in forecasting and provide guidelines for modeling and novel applications in future studies, this review focuses on applications of RNNs for time series forecasting in environmental factor forecasting. We present the structure, processing flow, and advantages of RNNs and analyze the applications of various RNNs in time series forecasting. In addition, we discuss limitations and challenges of applications based on RNNs and future research directions. Finally, we summarize applications of RNNs in forecasting.

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