Short term load forecasting using data mining technique

Razak, Ibnu; Majid, Shah; Rahman, Hasimah Abdul; Hassan, Mohammad Yusri

doi:10.1109/pecon.2008.4762460

Cited by 7 publications

(1 citation statement)

References 7 publications

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“…In this reference, the short‐term energy forecasting has been studied for two categories of holidays and other ordinary days using artificial neural network method. To have more accuracy in short‐term energy forecasting, future weather situations and predictions, especially temperature changes in different seasons, are very important . The effect of active loads has been studied in Paoletti et al In forecasting the future energy, the programmers commonly consider the average of loads.…”

Section: Introductionmentioning

confidence: 99%

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%