Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks

Abdel-Aal, R.E.

doi:10.1016/j.cie.2007.10.020

Cited by 101 publications

(36 citation statements)

References 30 publications

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“…Furthermore, compared with short-term load data (e.g., 96-point daily ones), monthly data often contain more information. To date, although NN has been widely used for monthly data forecasting, its applications are limited to either special trends [16] or special points (e.g., peak load prediction) [17].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Monthly Electric Energy Consumption Using Feature Extraction

2011

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Monthly forecasting of electric energy consumption is important for planning the generation and distribution of power utilities. However, the features of this time series are so complex that directly modeling is difficult. Three kinds of relatively simple series can be derived when a discrete wavelet transform is used to extract the raw features, namely, the rising trend, periodic waves, and stochastic series. After the elimination of the stochastic series, the rising trend and periodic waves were modeled separately by a grey model and radio basis function neural networks. Adding the forecasting values of each model can yield the forecasting results for monthly electricity consumption. The grey model has a good capability for simulating any smoothing convex trend. In addition, this model can mitigate minor stochastic effects on the rising trend. The extracted periodic wave series, which contain relatively less information and comprise simple regular waves, can improve the generalization capability of neural networks. The case study on electric energy consumption in China shows that the proposed method is better than those traditionally used in terms of both forecasting precision and expected risk.

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Section: Introductionmentioning

confidence: 99%

Forecasting Monthly Electric Energy Consumption Using Feature Extraction

2011

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“…In this situation, the "black box" syndrome is not a large drawback. In the energy sector, ANNs have been implemented for long-term load forecasting [54], for monthly energy demand [55], for forecasting daily urban electric load profiles [56] and for forecasting electricity market prices [57].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

3-Domain Modelling

Mirakyan

Guio

2015

Three Domain Modelling and Uncertainty Analysis

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“…A. Azadeh and et al, investigated and predicted electrical energy consumption using artificial neural networks and genetic algorithm [10]. R. E. Abdel-Aal had published univariate modelling and forecasting method of monthly energy demand using time series and neural networks [11]. Kadir Kavaklioglu analysed energy consumption with socioeconomic and demographic variables (GNP, Population, Import, and Exports), then researched modelling and prediction of energy consumption in Turkey by using support vector regression [12].…”

Section: Related Workmentioning

confidence: 99%