Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach

Chang, Pei‐Chann; Fan, Chin-Yuan; Lin, Jiunn-Yuan

doi:10.1016/j.ijepes.2010.08.008

Cited by 145 publications

(56 citation statements)

References 33 publications

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“…Although several different forecasting methods are used for prediction of electricity demand, none of them is superior in all cases. Some of these techniques used to forecast electricity demand of countries are the time series model (Saab et al, 2001;Sa'ad, 2009;Dilaver and Hunt, 2011;Boran, 2014;Efendi et al, 2014), artificial neural networks (ANNs) model (Hamzacebi and Kutay, 2004;Hamzacebi, 2007;Azadeh et al, 2008;Cunkas and Altun, 2010;Panklib et al, 2015) , regression and econometric model (Mohamed and Bodger, 2005;Al-Shobaki and Mohsen, 2008;Meng and Niu, 2011;Bildirici et al, 2012;Bianco et al, 2013), neuro-fuzyy model (Demirel et al, 2010;Chang et al, 2011), heuristic optimization method (El-Telbany and ElKarmi, 2008;Cunkas and Taskiran, 2011;Zhu et al, 2011), and support vector regression model (SVR) (De Felice et al, 2015;Jain et al, 2014;Kaytez et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Primary energy sources planning based on demand forecasting: The case of Turkey

Hamzaçebi

2016

J. energy South. Afr.

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Section: Introductionmentioning

confidence: 99%

Primary energy sources planning based on demand forecasting: The case of Turkey

Hamzaçebi

2016

J. energy South. Afr.

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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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“…Meanwhile, artificial neural networks and support vector machines have a variety of applications in electric load forecasting. Chang et al proposed the so-called EEuNN framework by adopting a weighted factor to calculate the importance of each factor among the different rules to predict monthly electricity demand in Taiwan [4]. Kavaklioglu used SVR to model and predict Turkey's electricity consumption [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting

Kong

2017

Energies

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Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM), which combines k-Nearest Neighbor (KNN) and Extreme Learning Machine (ELM) based on a wavelet denoising technique is proposed for short-term load forecasting. The proposed hybrid model decomposes the time series into a low frequency-associated main signal and some detailed signals associated with high frequencies at first, then uses KNN to determine the independent and dependent variables from the low-frequency signal. Finally, the ELM is used to get the non-linear relationship between these variables to get the final prediction result for the electric load. Compared with three other models, Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EKM), Wavelet Denoising-Extreme Learning Machine (WKM) and Wavelet Denoising-Back Propagation Neural Network optimized by k-Nearest Neighbor Regression (WNNM), the model proposed in this paper can improve the accuracy efficiently. New South Wales is the economic powerhouse of Australia, so we use the proposed model to predict electric demand for that region. The accurate prediction has a significant meaning.

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Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach

Cited by 145 publications

References 33 publications

Primary energy sources planning based on demand forecasting: The case of Turkey

Primary energy sources planning based on demand forecasting: The case of Turkey

Forecasting Monthly Electric Energy Consumption Using Feature Extraction

A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting

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