A developed hybrid forecasting system for energy consumption structure forecasting based on fuzzy time series and information granularity

Energy ◽  
2021 ◽  
Vol 219 ◽  
pp. 119599
Author(s):  
Ping Jiang ◽  
Hufang Yang ◽  
Hongmin Li ◽  
Ying Wang
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Fuzzy Time Series ◽  
Information Granularity ◽  
Consumption Structure ◽  
Hybrid Forecasting ◽  
Forecasting System

scholarly journals Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3588 ◽  
Author(s):  
Wei ◽  
Wang ◽  
Ni ◽  
Tang
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Short Term Memory ◽  
Electrical Power ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Grey Wolf Optimizer ◽  
Power Load ◽  
Hybrid Forecasting ◽  
Forecasting System

In recent years, although deep learning algorithms have been widely applied to various fields, ranging from translation to time series forecasting, researchers paid limited attention to modelling parameter optimization and the combination of the fuzzy time series. In this paper, a novel hybrid forecasting system, named CFML (complementary ensemble empirical mode decomposition (CEEMD)-fuzzy time series (FTS)-multi-objective grey wolf optimizer (MOGWO)-long short-term memory (LSTM)), is proposed and tested. This model is based on the LSTM model with parameters optimized by MOGWO, before which a fuzzy time series method involving the LEM2 (learning from examples module version two) algorithm is adopted to generate the final input data of the optimized LSTM model. In addition, the CEEMD algorithm is also used to de-noise and decompose the raw data. The CFML model successfully overcomes the nonstationary and irregular features of wind speed data and electrical power load series. Several experimental results covering four wind speed datasets and two electrical power load datasets indicate that our hybrid forecasting system achieves average improvements of 49% and 70% in wind speed and electrical power load, respectively, under the metric MAPE (mean absolute percentage error).

A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization

2011 ◽  
Vol 38 (7) ◽  
pp. 8014-8023 ◽  
Author(s):  
Yao-Lin Huang ◽  
Shi-Jinn Horng ◽  
Mingxing He ◽  
Pingzhi Fan ◽  
Tzong-Wann Kao ◽  
...  
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Swarm Optimization ◽  
Hybrid Forecasting

Fuzzy time series forecasting for supply chain disruptions

2015 ◽  
Vol 115 (3) ◽  
pp. 419-435 ◽  
Author(s):  
Felix T.S. Chan ◽  
Avinash Samvedi ◽  
S.H. Chung
Keyword(s):  
Time Series ◽  
Supply Chain ◽  
Moving Average ◽  
Discrete Event ◽  
Bullwhip Effect ◽  
Fuzzy Time Series ◽  
Supply Chain Disruptions ◽  
Content Type ◽  
Forecasting System ◽  
A Chain

Purpose – The purpose of this paper is to test the effectiveness of fuzzy time series (FTS) forecasting system in a supply chain experiencing disruptions and also to examine the changes in performance as the authors move across different tiers. Design/methodology/approach – A discrete event simulation based on the popular beer game model is used for these tests. A popular ordering management system is used to emulate the behavior of the system when the game is played with human players. Findings – FTS is tested against some other well-known forecasting systems and it proves to be the best of the lot. It is also shown that it is better to go for higher order FTS for higher tiers, to match auto regressive integrated moving average. Research limitations/implications – This study fills an important research gap by proving that FTS forecasting system is the best for a supply chain during disruption scenarios. This is important because the forecasting performance deteriorates significantly and the effect is more pronounced in the upstream tiers because of bullwhip effect. Practical implications – Having a system which works best in all scenarios and also across the tiers in a chain simplifies things for the practitioners. The costs related to acquiring and training comes down significantly. Originality/value – This study contributes by suggesting a forecasting system which works best for all the tiers and also for every scenario tested and simultaneously significantly improves on the previous studies available in this area.

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
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