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All Journal International Journal of Electrical and Computer Engineering Advance Sustainable Science, Engineering and Technology (ASSET)
Etna Vianita
Universitas Diponegoro
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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The cross-association relation based on intervals ratio in fuzzy time series Etna Vianita; Muhammad Sam'an; A. Nafis Haikal; Ina Salamatul Mufaricha; Redemtus Heru Tjahjana; Titi Udjiani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2040-2051

Abstract

The fuzzy time series (FTS) is a forecasting model based on linguistic values. This forecasting method was developed in recent years after the existing ones were insufficiently accurate. Furthermore, this research modified the accuracy of existing methods for determining and the partitioning universe of discourse, fuzzy logic relationship (FLR), and variation historical data using intervals ratio, cross association relationship, and rubber production Indonesia data, respectively. The modified steps start with the intervals ratio to partition the determined universe discourse. Then the triangular fuzzy sets were built, allowing fuzzification. After this, the FLR are built based on the cross-association relationship, leading to defuzzification. The average forecasting error rate (AFER) was used to compare the modified results and the existing methods. Additionally, the simulations were conducted using rubber production Indonesia data from 2000-2020. With an AFER result of 4.77%<10%, the modification accuracy has a smaller error than previous methods, indicating very good forecasting criteria. In addition, the coefficient values of D1 and D2 were automatically obtained from the intervals ratio algorithm. The future works modified the partitioning of the universe of discourse using frequency density to eliminate unused partition intervals.
Multi-Horizon Short-Term Residential Load Forecasting Using Decomposition-Based Linear Neural Network Henri Tantyoko; Satriawan Rasyid Purnama; Etna Vianita
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2033

Abstract

Short-Term Load Forecasting is crucial for grid stability and real-time energy management, particularly in residential settings where consumption is highly volatile and influenced by behavioral and external factors. Traditional models struggle to capture complex, non-linear patterns. This study proposes a forecasting framework based on the DLinear model, which decomposes time series data into trend and seasonal components using a simple linear neural network architecture. Designed for multi-horizon forecasting, the model predicts electricity demand across several future time points simultaneously. Experimental results show that DLinear performs best at a 24-hour prediction length, achieving the lowest MSE of 41.58 and MAE of 5.11, indicating improved accuracy with longer horizons. These results confirm DLinear’s robustness and efficiency in modeling dynamic residential electricity consumption patterns.
Co-Authors A. Nafis Haikal Henri Tantyoko Ina Salamatul Mufaricha Muhammad Sam&#039;an Redemtus Heru Tjahjana Satriawan Rasyid Purnama Titi Udjiani