scholarly journals Forecasting Irregular Seasonal Power Consumption. An Application to a Hot-Dip Galvanizing Process

2020 ◽  
Vol 11 (1) ◽  
pp. 75
Author(s):  
Oscar Trull ◽  
Juan Carlos García-Díaz ◽  
Angel Peiró-Signes
Keyword(s):  
Time Series ◽  
Electricity Consumption ◽  
Seasonal Component ◽  
Real Situation ◽  
Forecasting Methods ◽  
Sufficient Energy ◽  
Discrete Interval ◽  
Related Time Series ◽  
Strong Seasonality ◽  
Grid Management

Distribution companies use time series to predict electricity consumption. Forecasting techniques based on statistical models or artificial intelligence are used. Reliable forecasts are required for efficient grid management in terms of both supply and capacity. One common underlying feature of most demand–related time series is a strong seasonality component. However, in some cases, the electricity demanded by a process presents an irregular seasonal component, which prevents any type of forecast. In this article, we evaluated forecasting methods based on the use of multiple seasonal models: ARIMA, Holt-Winters models with discrete interval moving seasonality, and neural networks. The models are explained and applied to a real situation, for a node that feeds a galvanizing factory. The zinc hot-dip galvanizing process is widely used in the automotive sector for the protection of steel against corrosion. It requires enormous energy consumption, and this has a direct impact on companies’ income statements. In addition, it significantly affects energy distribution companies, as these companies must provide for instant consumption in their supply lines to ensure sufficient energy is distributed both for the process and for all the other consumers. The results show a substantial increase in the accuracy of predictions, which contributes to a better management of the electrical distribution.

scholarly journals SARIMA-Orthogonal Polynomial Curve Fitting Model for Medium-Term Load Forecasting

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Herui Cui ◽  
Pengbang Wei ◽  
Yupei Mu ◽  
Xu Peng
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Load Forecasting ◽  
Electricity Consumption ◽  
Seasonal Component ◽  
Medium Term ◽  
Model Order Selection ◽  
Polynomial Curve ◽  
Key Factor ◽  
Hp Filter

Seasonal component has been a key factor in time series modeling for medium-term electric load forecasting. In this paper, a seasonal-ARIMA model is developed, but the parameters of the SAR and the SMA turn out to be quite nonsignificant in most cases during the model order selection. To address this issue, the hybrid time series model based on the HP filter is utilized to extract the spectrum sequences with different frequencies and analyze interactions among various factors. Finally, an integrative forecast is made for the electricity consumption from January to November in 2014. The empirical results demonstrate that the method with HP filter could reduce the relative error caused by the interaction between the trend component and the seasonal component.

A Novel Intuitionistic Fuzzy Time Series Method Based on Bootstrapped Combined Pi-Sigma Artificial Neural Network

2021 ◽  
Vol 01 ◽  
Author(s):  
Eren Bas ◽  
Erol Egrioglu ◽  
Emine Kölemen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Time Series Method ◽  
Artificial Neural

Background: Intuitionistic fuzzy time series forecasting methods have been started to solve the forecasting problems in the literature. Intuitionistic fuzzy time series methods use both membership and non-membership values as auxiliary variables in their models. Because intuitionistic fuzzy sets take into consideration the hesitation margin and so the intuitionistic fuzzy time series models use more information than fuzzy time series models. The background of this study is about intuitionistic fuzzy time series forecasting methods. Objective: The study aims to propose a novel intuitionistic fuzzy time series method. It is expected that the proposed method will produce better forecasts than some selected benchmarks. Method: The proposed method uses bootstrapped combined Pi-Sigma artificial neural network and intuitionistic fuzzy c-means. The combined Pi-Sigma artificial neural network is proposed to model the intuitionistic fuzzy relations. Results and Conclusion: The proposed method is applied to different sets of SP&500 stock exchange time series. The proposed method can provide more accurate forecasts than established benchmarks for the SP&500 stock exchange time series. The most important contribution of the proposed method is that it creates statistical inference: probabilistic forecasting, confidence intervals and the empirical distribution of the forecasts. Moreover, the proposed method is better than the selected benchmarks for the SP&500 data set.

scholarly journals Forecasting emergency department presentations

2007 ◽  
Vol 31 (1) ◽  
pp. 83 ◽  
Author(s):  
Robert Champion ◽  
Leigh D Kinsman ◽  
Geraldine A Lee ◽  
Kevin A Masman ◽  
Elizabeth A May ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Time Series Analysis ◽  
Software Package ◽  
Exponential Smoothing ◽  
Statistical Forecasting ◽  
Forecasting Methods ◽  
Number Of Patients ◽  
Forecasting Performance ◽  
Optimal Forecasting

Objective: To forecast the number of patients who will present each month at the emergency department of a hospital in regional Victoria. Methods: The data on which the forecasts are based are the number of presentations in the emergency department for each month from 2000 to 2005. The statistical forecasting methods used are exponential smoothing and Box?Jenkins methods as implemented in the software package SPSS version 14.0 (SPSS Inc, Chicago, Ill, USA). Results: For the particular time series, of the available models, a simple seasonal exponential smoothing model provides optimal forecasting performance. Forecasts for the first five months in 2006 compare well with the observed attendance data. Conclusions: Time series analysis is shown to provide a useful, readily available tool for predicting emergency department demand. The approach and lessons from this experience may assist other hospitals and emergency departments to conduct their own analysis to aid planning.

scholarly journals A novel deseasonalized time series model with an improved seasonal estimate for groundwater level predictions

2019 ◽  
Vol 2 (1) ◽  
pp. 25-44 ◽  
Author(s):  
S. Mohanasundaram ◽  
G. Suresh Kumar ◽  
Balaji Narasimhan
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Moving Average ◽  
Seasonal Component ◽  
Univariate Time Series ◽  
Level Data ◽  
Proposed Model ◽  
Interval Approach ◽  
Prediction And Forecasting ◽  
Observation Wells

Abstract Groundwater level prediction and forecasting using univariate time series models are useful for effective groundwater management under data limiting conditions. The seasonal autoregressive integrated moving average (SARIMA) models are widely used for modeling groundwater level data as the groundwater level signals possess the seasonality pattern. Alternatively, deseasonalized autoregressive and moving average models (Ds-ARMA) can be modeled with deseasonalized groundwater level signals in which the seasonal component is estimated and removed from the raw groundwater level signals. The seasonal component is traditionally estimated by calculating long-term averaging values of the corresponding months in the year. This traditional way of estimating seasonal component may not be appropriate for non-stationary groundwater level signals. Thus, in this study, an improved way of estimating the seasonal component by adopting a 13-month moving average trend and corresponding confidence interval approach has been attempted. To test the proposed approach, two representative observation wells from Adyar basin, India were modeled by both traditional and proposed methods. It was observed from this study that the proposed model prediction performance was better than the traditional model's performance with R2 values of 0.82 and 0.93 for the corresponding wells' groundwater level data.

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