A hybrid TSK-FR model to study short-term variations of the electricity demand versus the temperature changes

Nadimi, Reza; Ghaderi, F.

doi:10.1016/j.eswa.2007.12.058

Cited by 43 publications

(9 citation statements)

References 19 publications

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“…Duran Toksari (2007Toksari ( , 2009) utilized the ant colony optimization approach models for forecasting net electricity energy consumption for Turkey until 2025. Shakouri et al (2009) introduced a type III TSK fuzzy inference machine combined with a set of linear and nonlinear fuzzy regressors to model effects of the climate change on the electricity demand, and they clustered the data for the optimization model. However, these aforementioned methods need a lot of data and, therefore, cannot be directly applied for time-series forecasting with little data.…”

Section: Introductionmentioning

confidence: 99%

A Grey Prediction Approach to Forecasting Energy Demand in China

Liu

Qin

2010

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Energy demand forecasting plays an important role in decision making. A mathematical model known as grey model GM(1,1) has been, herewith, employed successfully in the estimation of energy demand. In order to improve the forecast accuracy, the original GM(1,1) models are improved by using three methodologies of the 3-points average technology and the residual modification. This method takes into account the general trend series and random fluctuations about this trend. Two experiments were carried out respectively on the electricity demand and energy production from 1984 to 2006 in China to demonstrate the effectiveness of our approach. Furthermore, this improved grey forecasting model was used to forecast China's electricity demand and energy production, which shows that the modified forecasting model is more reliable and has a higher forecast accuracy than the GM (1,1). The forecasted results indicate that China's final energy demand will increase rapidly in the period 2007-2015. The results provide scientific basis for the planned development of energy supply in China.

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Section: Introductionmentioning

confidence: 99%

A Grey Prediction Approach to Forecasting Energy Demand in China

Liu

Qin

2010

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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“…Recently, both theoretical and empirical results have suggested that combining forecasting methods can be an effective way to achieve better predictive performance over individual models [12,13]. Contributions from many researchers have improved the quality of the predictions and provided combined forecasting models for decision makers [14][15][16][17][18]. As a result, there have been profound changes in the forecasting field.…”

Section: Literature Reviewmentioning

confidence: 99%

New Markets Forecast and Dynamic Production Redesign Through Stochastic Simulation

Bendato¹,

Mosca²

2015

Int. j. simul. model.

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In this paper the authors analyse a double problem: the sales forecast in new foreign markets in the next five years by an Italian SME and the consequent dynamic production system redesign having the goal to maximize the Gross Margin.Due to the high uncertainty of the variables involved, the authors approached the study in stochastic regime. The methodology is based on Monte Carlo and Discrete Event Simulation, on new Pure Experimental Error control techniques and on Response Surface Methodology Designs as the Two Level Factorial Design and the Central Composite Design.

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“…In another study, Azadeh et al proposed a fuzzy regression algorithm for oil demand estimation of the U.S., Canada, Japan and Australia [11]. A hybrid technique of well-known Takagi-Sugeno fuzzy inference system and fuzzy regression has been proposed for prediction of short-term electric demand variations by Shakouri et al [12]. In their study, Shakouri et al introduced a type III TSK fuzzy inference machine combined with a set of linear and nonlinear fuzzy regressors in the consequent part to model effects of the climate change on the electricity demand.…”

Section: Introductionmentioning

confidence: 99%