Long-range forecasting for utilities with dynamic load growth rates

Elrazaz, Z.; Al-Mohawes, N.; Mazi, A. A.

doi:10.1109/cjece.1988.6593009

Cited by 2 publications

(3 citation statements)

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“…In literature, there are different methods for forecasting the future energy . In Li et al a method has been proposed for short‐term load forecasting based on wavelet transform and partial least squares regression.…”

Section: Introductionmentioning

confidence: 99%

“…To select proper input features among the feature candidates, including time lagged loads as well as temperatures, a mimetic algorithm based on the firefly algorithm has been used. In Elrazaz et al a long‐term method for forecasting the industrial and residential load growth pattern according to peak demand and energy consumption have been presented. A long‐term forecasting comparison between developing and developed economies has been studied in Ardakani and Ardehali .…”

Section: Introductionmentioning

confidence: 99%

“…In literature, there are different methods for forecasting the future energy. [1][2][3][4][5][6][7][8][9] In Li et al 4 a method has been proposed for short-term load forecasting based on wavelet transform and partial least squares regression. In another research, hourly loads and peak load for the next selected time span have been predicted using hourly loads of successive years.…”

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confidence: 99%

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A new combined method for future energy forecasting in electrical networks

Baesmat

Shiri

2018

Int Trans Electr Energ Syst

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Summary Growing the industry and population in a region leads to the growth of required amount of electrical energy. Electrical companies should provide high quality energy according to the demand of customers. Load is an effective parameter of any system, and the network programmers should consider the annual load growth of the system and predict the required energy for each region by investigating previously recorded data of consumed electrical energy and stochastic analysis. In this paper, a new combined method for long‐term energy forecasting is proposed. This method, which combines the land‐consumption method and curve fitting based on generalization method, in addition to having simple calculations, takes into account the saturation. Moreover, loads from detailed formal program provided by the relevant institutes have been used, which results in better coordination between all organizations in charge of energy predictions and development of the countries. A suitable filtering method is also employed for input data to improve the method accuracy. To show the effectiveness of the proposed method, the results of different methods have been compared with those of the proposed method as well as real data. Then, real data of former 11 years of consumed energy gathered from Shiraz Electrical Distribution Company subscribers are employed and the energy for future 11 years is forecasted.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%