Prediction of Energy Consumption in Jiangsu Based on Combination Model

Chen, Jibo; Huang, Xiaorong; Li, Jie; Wang, Guizhi

doi:10.1109/icic.2010.298

Cited by 2 publications

(2 citation statements)

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“…Autoregressive integrated moving average (ARIMA) is a vastly used time series approach. ARIMA model was used by Chen J. et al [7] to predict energy consumption in Jiangsu province in China based on data collected from year 1985 to 2007. The model [7] was able to accurately predict the energy consumption, however it was limited to that environment.…”

Section: Introductionmentioning

confidence: 99%

“…ARIMA model was used by Chen J. et al [7] to predict energy consumption in Jiangsu province in China based on data collected from year 1985 to 2007. The model [7] was able to accurately predict the energy consumption, however it was limited to that environment. The Previous works on time series usually use computationally complex matrix-oriented adaptive algorithms which, in most scenarios, may become unstable.…”

Section: Introductionmentioning

confidence: 99%

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Using Artificial Neural Network Techniques for Prediction of Electric Energy Consumption

Owda¹,

Omoniwa²,

Raza³

et al. 2014

Preprint

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Due to imprecision and uncertainties in predicting real world problems, artificial neural network (ANN) techniques have become increasingly useful for modeling and optimization. This paper presents an artificial neural network approach for forecasting electric energy consumption. For effective planning and operation of power systems, optimal forecasting tools are needed for energy operators to maximize profit and also to provide maximum satisfaction to energy consumers. Monthly data for electric energy consumed in the Gaza strip was collected from year 1994 to 2013. Data was trained and the proposed model was validated using 2-Fold and K-Fold cross validation techniques. The model has been tested with actual energy consumption data and yields satisfactory performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%