Forecasting for the Total Electricity Consumption of Taiwan by Fuzzy Time Series Model

2018 ◽  
pp. 220-225
Author(s):  
Jing-Rong Chang ◽  
Zhong-Qi Liu ◽  
Pei-Yu Yu
Keyword(s):  
Time Series ◽  
Electricity Consumption ◽  
Time Series Model ◽  
Fuzzy Time Series

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

scholarly journals FUZZY TIME SERIES MODEL CHENG UNTUK MERAMALKAN VOLUME HASIL PANEN PADA TANAMAN GARUT

Telematika ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 11
Author(s):  
Rifki Indra Perwira ◽  
Danang Yudhiantoro ◽  
Endah Wahyurini
Keyword(s):  
Time Series ◽  
Seed Quality ◽  
Crop Yields ◽  
Genetic Data ◽  
Time Series Model ◽  
Environmental Data ◽  
Fuzzy Time Series ◽  
Market Demand ◽  
Cultivation Technique ◽  
Processed Products

Arrowroot is an alternative food substitute that can be used as processed flour or starch. This arrowroot can also produce several processed products such as arrowroot chips. The number of arrowroot requests from various regions causes the need for accurate calculations related to the volume of harvest from the arrowroot. Fuzzy logic is a method that can be used to predict arrowroot yields every period to meet market demand. The parameters used in this system are based on environmental data (temperature humidity, climate, altitude), genetic data (age and variety), and cultivation technique data (seed quality, fertilizing, planting media). The results of this study are in the form of an application to predict the volume of arrowroot crop yields based on these parameters. From the results of MAPE, get a percentage of 11.7% which indicates that the level of accuracy using the fuzzy cheng time series model is said to be useful for forecasting on arrowroot plants.

scholarly journals Forecasting Electricity Consumption Using Fuzzy Time Series

2018 ◽  
Vol 7 (4.30) ◽  
pp. 342
Author(s):  
K.G. Tay ◽  
Y.Y. Choy ◽  
C.C. Chew
Keyword(s):  
Time Series ◽  
Membership Function ◽  
Electricity Consumption ◽  
Fuzzy Time Series ◽  
Infrastructure Development ◽  
Power Load ◽  
Increasing Demand ◽  
Facility Expansion ◽  
Electric Power Load ◽  
Monthly Electricity Consumption

Electricity consumption forecasting is important for effective operation, planning and facility expansion of power system.  Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development.  There is a great development of Universiti Tun Hussein Onn Malaysia (UTHM) infrastructure since its formation in 1993. The development will be accompanied with the increasing demand of electricity.  Hence, there is a need to forecast the UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. Therefore, in this study, the Fuzzy time series (FTS) with trapezoidal membership function was implemented on the UTHM monthly electricity consumption from January 2011 to December 2017 to forecast January to December 2018 monthly electricity consumption.  The procedure of the FTS and trapezoidal membership function was described together with January data.  FTS is able to forecast UTHM electricity consumption quite well.

A new fuzzy time-series model of fuzzy number observations

1995 ◽  
Vol 73 (3) ◽  
pp. 341-348 ◽  
Author(s):  
Qiang Song ◽  
Robert P. Leland ◽  
Brad S. Chissom
Keyword(s):  
Time Series ◽  
Fuzzy Number ◽  
Time Series Model ◽  
Fuzzy Time Series
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