The implementation of long-term forecasting strategies using a knowledge-based expert system: part-II

Kandil, M.S.; El-Debeiky, S.; Hasanien, N.E.

doi:10.1016/s0378-7796(01)00098-0

Cited by 36 publications

(10 citation statements)

References 1 publication

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“…More specifically, the improvement being achieved for the mean forecasting value is greater than 0.2% for the second order regression model and greater than 2.1% for all the other models. It should be noted that the accuracy of the proposed method is very satisfactory when compared with the results obtained by the annual energy forecasting methods in [4,11,26,29,30,32,35,36,40], where the respective MAPE varies between 0.60% and 8.4%. Only Kandil et al [32] have reported smaller MAPE (0.60%) than the proposed model.…”

Section: Annual Energy Forecasting For the Greek Power Systemmentioning

confidence: 63%

“…In Refs. [31][32][33] a knowledge-based expert system has been implemented to support the choice of the most suitable long-term peak load forecasting model giving better results than the classical ones. Alternatively, decision support systems [34], support vector machines using simulated annealing algorithms [26] or genetic ones [35], models based on dynamic simulation theory (GSIM) [36] and Grey methodology [37] have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

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A non-linear multivariable regression model for midterm energy forecasting of power systems

Tsekouras

Dialynas

Hatziargyriou

et al. 2007

Electric Power Systems Research

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Section: Annual Energy Forecasting For the Greek Power Systemmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

A non-linear multivariable regression model for midterm energy forecasting of power systems

Tsekouras

Dialynas

Hatziargyriou

et al. 2007

Electric Power Systems Research

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“…Examples are genetic algorithm [54][55][56], expert system [57][58][59][60], and evolutionary computation algorithms [61].…”

Section:  Other Hybrid Methodsmentioning

confidence: 99%

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

134

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Load forecasting has always been an important part in the planning and operation of electric utilities, i.e., both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This paper aims at reviewing the different load forecasting techniques developed for the mid-and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid-and long-term horizons, this paper reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. The study is concluded with discussion on future research directions.

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“…The offspring generation is generated according to Equations (9)(10)(11)(12)(13)(14). Then input the offspring into the LSSVM model and calculate the smell concentration value again.…”

Section: Step4: Offspring Generationmentioning

confidence: 99%

“…Chen [12] proposed a collaborative fuzzy-neural approach for forecasting Taiwan's annual electricity load, and this approach could improve the forecasting accuracy. Kandil et al [13] implemented a knowledge-based expert system to support the choice of the most suitable load forecasting model, and the usefulness of this method was demonstrated by a practical application. Hong [14] proposed an electric load forecasting model which combined the seasonal recurrent support vector regression model with a chaotic artificial bee colony algorithm, and this method could provide a more accurate forecasting result than the TF-ε-SVR-SA and ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

et al. 2012

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Abstract:The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA) has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA) outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA), generalized regression neural network (GRNN) and regression model.

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The implementation of long-term forecasting strategies using a knowledge-based expert system: part-II

Cited by 36 publications

References 1 publication

A non-linear multivariable regression model for midterm energy forecasting of power systems

A non-linear multivariable regression model for midterm energy forecasting of power systems

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

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