scholarly journals Prediction of Diesel Fired Power Plant Feeder Performance using First Order Fuzzy Time Series

2019 ◽  
Vol 8 (11S) ◽  
pp. 813-819
Keyword(s):  
Time Series ◽  
Power Plant ◽  
Fuzzy Time Series ◽  
Electricity Supply ◽  
Power Outages ◽  
Power Outage ◽  
First Order ◽  
Variable Duration

Power outages caused by factors outside the established policy will have an impact on the decline in electricity supply services and other cost related impacts. The reliability of the power plant feeder, in this case, is very important to monitor and maintain. The performance of power plant feeder can be reviewed based on the variable duration of power outage and power which fails to distribute. In this study, 1st order FTS (Fuzzy Time Series) is used to predict the feeder's performance through the predictive activity of both those variables in the actual year and the following year. The prediction results state that in 2017 there was a 20.54% decrease in performance

scholarly journals Scheduling Maintenance of Cooling System based on Risk Priority Number (RPN) using Adaptive Neural Network

2019 ◽  
Vol 8 (11S) ◽  
pp. 807-812
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Power Plant ◽  
Cooling System ◽  
Fuzzy Time Series ◽  
Electricity Supply ◽  
Risk Priority Number ◽  
Adaptive Neural Network ◽  
Power Outage ◽  
Variable Duration

Power outages caused by factors outside the established policy will have an impact on the decline in electricity supply services and other cost related impacts. The reliability of the power plant feeder, in this case, is very important to monitor and maintain. The performance of power plant feeder can be reviewed based on the variable duration of power outage and power which fails to distribute. In this study, 1st order FTS (Fuzzy Time Series) is used to predict the feeder's performance through the predictive activity of both those variables in the actual year and the following year. The prediction results state that in 2017 there was a 20.54% decrease in performance.

Der volkswirtschaftliche Verlust durch Stromausfälle: Eine empirische Analyse für Haushalte, Unternehmen und den öffentlichen Sektor

2012 ◽  
Vol 13 (4) ◽  
pp. 308-336 ◽  
Author(s):  
Michael Schmidthaler ◽  
Johannes Reichl ◽  
Friedrich Schneider
Keyword(s):  
Economic Value ◽  
Cost Benefit ◽  
Time Of Day ◽  
Electricity Supply ◽  
Analysis Tool ◽  
Power Outages ◽  
Power Outage ◽  
Average Value ◽  
Precise Knowledge ◽  
Future Challenges

AbstractThis work discusses different methodological approaches for the economic evaluation of electricity supply security, quantifies the expected economic costs of power outages in Austria, and provides an interpretation of the results regarding the future challenges of sustaining the currently high levels of electricity supply security. By applying a macroeconomic simulation tool, which assesses the damages of power outages which can be defined for the period between one to 48 hours taking into account the day of the week and time of day, the value of supply security can be estimated precisely with high spatial and sectoral resolution. This is demonstrated exemplarily for a power outage scenario which is similar in scope, timing and duration to a historic even in Italy in 2003 affecting over 50 million people. Decision-makers in politics and businesses can use the analysis tool APOSTEL to conduct precise evaluations of the value of supply security, for cost-benefit analyses of supply security enhancing investments, of regulatory descions which affect the level of supply securty and for many more applications with regards to energy policy. Precise knowledge of the social and economic value of a secure supply of electricity becomes even more crucial considering that the average value of lost load for a one-hour power cut in Austria on a weekday morning in the summer is calculated at 17.1 € per kWh of electricity not supplied.

scholarly journals First-Order ARMA Type Fuzzy Time Series Method Based on Fuzzy Logic Relation Tables

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Cem Kocak
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Stock Exchange ◽  
Life Time ◽  
Fuzzy Time Series ◽  
Moving Averages ◽  
First Order ◽  
Forecasting Method ◽  
Forecasting Performance ◽  
Time Series Method

Fuzzy time series approaches have an important deficiency according to classical time series approaches. This deficiency comes from the fact that all of the fuzzy time series models developed in the literature use autoregressive (AR) variables, without any studies that also make use of moving averages (MAs) variables with the exception of only one study (Egrioglu et al. (2013)). In order to eliminate this deficiency, it is necessary to have many of daily life time series be expressed with Autoregressive Moving Averages (ARMAs) models that are based not only on the lagged values of the time series (AR variables) but also on the lagged values of the error series (MA variables). To that end, a new first-order fuzzy ARMA(1,1) time series forecasting method solution algorithm based on fuzzy logic group relation tables has been developed. The new method proposed has been compared against some methods in the literature by applying them on Istanbul Stock Exchange national 100 index (IMKB) and Gold Prices time series in regards to forecasting performance.

scholarly journals PERAMALAN MENGGUNAKAN METODE WEIGHTED FUZZY INTEGRATED TIME SERIES (Studi Kasus: Harga Beras di Indonesia Bulan Januari 2011 s/d Desember 2017)

2019 ◽  
Vol 8 (4) ◽  
pp. 518-529
Author(s):  
Setya Adi Rahmawan ◽  
Diah Safitri ◽  
Tatik Widiharih
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
High Order ◽  
Training Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
First Order ◽  
Rice Prices ◽  
Integrated Time Series

Fuzzy Time Series (FTS) is a time series data forecasting technique that uses fuzzy theory concepts. Forecasting systems using FTS are useful for capturing patterns of past data and then to using it to produce information in the future. Initially in the FTS each pattern of relations formed was considered to have the same weight besides using only the first order. In its development the Weighted Fuzzy Integrated Time Series (WFITS) which gave a difference in the weight of each relation and high order usage has been appeared. Measuring the accuracy of forecasting results is used the value of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). In this study both the first-order and high-order WFITS methods were applied to forecast rice prices in Indonesia based on data from January 2011 to December 2017. In this regard, the results of the analysis obtained data forecasting using Lee's high-order model WFITS algorithm (1,2,3) giving the value of RMSE and MAPE on the data testing in a row as many as 69,898 and 0.47% while for the RMSE and MAPE on the training data is as many as 70.4039 and 0.54%. Keywords: Fuzzy Time Series, Weighted Fuzzy Integrated Time Series, RMSE, MAPE, High-Order, Rice Prices

A Revised Fuzzy Time Series Method

2010 ◽  
Vol 171-172 ◽  
pp. 140-143
Author(s):  
Yan Hua Yu ◽  
Li Xia Song
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Fuzzy Regression ◽  
Fuzzy Time Series ◽  
Average Error ◽  
Series Method ◽  
First Order ◽  
Time Series Method ◽  
Time Invariant ◽  
Regression Theory

In this paper,we presents two methods to forecast secular trend and seasonal variation time series problems respectively. The revised fuzzy time series method uses Song and Chrisom’s first-order time-invariant model to predict such linguistic historical data problems. This method obtains a better average error than the error in Song and Chrisom’s method. The method using fuzzy regression theory solves the shortcoming that fuzzy time series method could not work in dealing with seasonal variation time series problems.

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

Peramalan Data IHSG Menggunakan Metode Backpropagation

2013 ◽  
Vol 5 (1) ◽  
pp. 26-30
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Soft Computing ◽  
Fuzzy Time Series

Jaringan saraf tiruan merupakan salah satu metode soft computing yang banyak digunakan dan diterapkan di berbagai disiplin ilmu, termasuk analisis data runtun waktu. Tujuan utama dari analisis data runtun waktu adalah untuk memprediksi data runtun waktu yang dapat digunakan secara luas dalam berbagai data runtun waktu real, termasuk data harga saham. Banyak peneliti yang telah berkontribusi dalam analisis data runtun waktu dengan menggunakan berbagai pendekatan berbeda. Chen dan Hsu, Jilani dkk., Stevenson dan Porter, dan Hansun telah menggunakan metode fuzzy time series untuk meramalkan data mendatang, sementara beberapa peneliti lainnya menggunakan metode hibrid, seperti yang dilakukan oleh Subanar dan Suhartono, Popoola dkk, Popoola, Hansun dan Subanar. Di dalam penelitian ini, penulis mencoba untuk menerapkan metode jaringan saraf tiruan backpropagation pada salah satu indikator perubahan harga saham, yakni IHSG (Indeks Harga Saham Gabungan). Penelitian dilanjutkan dengan menghitung tingkat akurasi dan kehandalan metode yang telah diterapkan pada data IHSG. Pendekatan ini diharapkan dapat menjadi salah satu cara alternatif dalam meramalkan data IHSG sebagai salah satu indikator perubahan harga saham di Indonesia. Kata kunci—jaringan saraf tiruan, backpropagation, analisis data runtun waktu, soft computing, IHSG

A Distributed Algorithm for Scalable Fuzzy Time Series

2019 ◽  
pp. 42-56 ◽  
Author(s):  
Petrônio Cândido de Lima e Silva ◽  
Patrícia de Oliveira e Lucas ◽  
Frederico Gadelha Guimarães
Keyword(s):  
Time Series ◽  
Distributed Algorithm ◽  
Fuzzy Time Series
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