Forecasting influent flow rate and composition with occasional data for supervisory management system by time series model

2006 ◽  
Vol 53 (4-5) ◽  
pp. 185-192 ◽  
Author(s):  
J.R. Kim ◽  
J.H. Ko ◽  
J.H. Im ◽  
S.H. Lee ◽  
S.H. Kim ◽  
...  
Keyword(s):  
Time Series ◽  
Flow Rate ◽  
Wastewater Treatment Plants ◽  
Model Development ◽  
Time Series Model ◽  
Spline Method ◽  
Forecasting Method ◽  
One Step ◽  
Simple Estimation ◽  
Accumulated Error

The information on the incoming load to wastewater treatment plants is not often available to apply modelling for evaluating the effect of control actions on a full-scale plant. In this paper, a time series model was developed to forecast flow rate, COD, NH+4-N and PO3-4-P in influent by using 250 days data of field plant operation data. The data for 150 days and 100 days were used for model development and model validation, respectively. The missing data were interpolated by the spline method and the time series model. Three different methods were proposed for model development: one model and one-step to seven-step ahead forecasting (Method 1); seven models and one-step-ahead forecasting (Method 2); and one model and one-step-ahead forecasting (Method 3). Method 3 featured only one-step-ahead forecasting that could avoid the accumulated error and give simple estimation of coefficients. Therefore, Method 3 was the reliable approach to developing the time series model for the purpose of this research.

One step-ahead ANFIS time series model for forecasting electricity loads

2009 ◽  
Vol 11 (2) ◽  
pp. 303-317 ◽  
Author(s):  
Ching-Hsue Cheng ◽  
Liang-Ying Wei
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
One Step

scholarly journals Pythagorean fuzzy time series model based on Pythagorean fuzzy c-means and improved Markov weighted in the prediction of the new COVID-19 cases

2021 ◽  
Author(s):  
Sidong Xian ◽  
Yue Cheng
Keyword(s):  
Time Series ◽  
Fuzzy Theory ◽  
Prediction Method ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
University Of Alabama ◽  
Fuzzy C Means ◽  
Forecasting Method ◽  
Fuzzy C Means Clustering ◽  
Realistic Prediction

Abstract Time series is an extremely important branch of prediction and the research on it plays an important guiding role in production and life. To get more realistic prediction results, scholars have explored the combination of fuzzy theory and time series. Although some results have been achieved so far, there are still gaps in the combination of n-Pythagorean fuzzy sets and time series. In this paper, a pioneering n-Pythagorean fuzzy time series model (n-PFTS) and its forecasting method (n-IMWPFCM) is proposed to employ a n-Pythagorean fuzzy c-means clustering method (n-PFCM) to overcome the subjectivity of directly assigning membership and non-membership values, thus improving the accuracy of the partition the universe of discoure. A novel improved Markov prediction method is exploited to enhance the prediction accuracy of the model. The proposed prediction method is applied to the yearly University of Alabama enrollments data and the new COVID-19 cases data. The results show that compared with the traditional fuzzy time series forecasting method, the proposed method has better forecasting accuracy. Meanwhile, it has the characteristics of low computational complexity and high interpretability and demonstrates the superiority of this model from a realistic perspective.

scholarly journals Mathematical Modeling of COVID-19 Control and Prevention Based on Immigration Population Data in China: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Qiangsheng Huang ◽  
Yu Sunny Kang
Keyword(s):  
Time Series ◽  
Model Development ◽  
Poor Performance ◽  
Population Data ◽  
Time Series Model ◽  
Prevention Measures ◽  
Control And Prevention ◽  
China Model ◽  
Highly Correlated ◽  
Epidemic Risk

BACKGROUND At the end of February 2020, the spread of coronavirus disease (COVID-19) in China had drastically slowed and appeared to be under control compared to the peak data in early February of that year. However, the outcomes of COVID-19 control and prevention measures varied between regions (ie, provinces and municipalities) in China; moreover, COVID-19 has become a global pandemic, and the spread of the disease has accelerated in countries outside China. OBJECTIVE This study aimed to establish valid models to evaluate the effectiveness of COVID-19 control and prevention among various regions in China. These models also targeted regions with control and prevention problems by issuing immediate warnings. METHODS We built a mathematical model, the Epidemic Risk Time Series Model, and used it to analyze two sets of data, including the daily COVID-19 incidence (ie, newly diagnosed cases) as well as the daily immigration population size. RESULTS Based on the results of the model evaluation, some regions, such as Shanghai and Zhejiang, were successful in COVID-19 control and prevention, whereas other regions, such as Heilongjiang, yielded poor performance. The evaluation result was highly correlated with the basic reproduction number (R<sub>0</sub>) value, and the result was evaluated in a timely manner at the beginning of the disease outbreak. CONCLUSIONS The Epidemic Risk Time Series Model was designed to evaluate the effectiveness of COVID-19 control and prevention in different regions in China based on analysis of immigration population data. Compared to other methods, such as R<sub>0</sub>, this model enabled more prompt issue of early warnings. This model can be generalized and applied to other countries to evaluate their COVID-19 control and prevention.

About One Approach to Defuzzifikation of Outputs of Fuzzy Time Series Model

2011 ◽  
Vol 3 (9) ◽  
pp. 562-566
Author(s):  
Ramin Rzayev ◽  
◽  
Musa Agamaliyev ◽  
Nijat Askerov
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
Fuzzy Time Series

Time Series Model of Wind Power Forecasting Error by using Beta Distribution for Optimal Sizing of Battery Storage

2019 ◽  
Vol 139 (3) ◽  
pp. 212-224
Author(s):  
Xiaowei Dui ◽  
Masakazu Ito ◽  
Yu Fujimoto ◽  
Yasuhiro Hayashi ◽  
Guiping Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Beta Distribution ◽  
Time Series Model ◽  
Battery Storage ◽  
Wind Power Forecasting ◽  
Optimal Sizing ◽  
Forecasting Error ◽  
Power Forecasting

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

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