scholarly journals Forecasting Electricity Consumption Using the Second-Order Fuzzy Time Series

2020 ◽  
Vol 932 ◽  
pp. 012056
Author(s):  
K. G. Tay ◽  
S.E. Sim ◽  
W. K. Tiong ◽  
Audrey Huong
Keyword(s):  
Time Series ◽  
Electricity Consumption ◽  
Second Order ◽  
Fuzzy Time Series

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.

scholarly journals Peramalan Jumlah Penumpang Kapal Laut Menggunakan Metode Fuzzy Runtun Waktu Chen Orde Tinggi

2021 ◽  
pp. 38-48
Author(s):  
Rizki Adiputra ◽  
Erna Tri Herdiani ◽  
Sitti Sahriman
Keyword(s):  
Time Series ◽  
Second Order ◽  
High Order ◽  
Fuzzy Time Series ◽  
Future Period ◽  
Fuzzy Method ◽  
Testing Data ◽  
The Future ◽  
Operational Activities

The port has become an important part of people's lives. On certain days there is an increase in the number of ship passengers which can slow down operational activities from the port, thus causing a buildup of passengers at the port. therefore, the port must be prepared to deal with the buildup of passengers at the port. Based on this, the researchers made a prediction or forecasting the number of ship passengers at Makassar Soekarno Hatta Port in the coming period to find out how much the estimated number of passengers at Makassar Soekarno Hatta Port. The results of these studies can be input to the PT. Pelabuhan Indonesia IV (Persero ) Makassar if there will be a surge in passengers in the future period. researchers used the fuzzy method of high order chen time series in forecasting or prediction in this study . The researcher divides the data onto training and testing data . The results of the study using fuzzy time series with the best high order chen are that the second order produces MAPE error size of 0,143 , MSE 13470993,9 and MAE of 9478,52 . The result of prediction of testing data onto one period in the future is 52.608.

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

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