A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran

Azadeh, A.; Ghaderi, S.F.; Sohrabkhani, S.

doi:10.1016/j.enpol.2008.02.035

Cited by 174 publications

(63 citation statements)

References 18 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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“…BP learning is a kind of supervised learning introduced by Werbos and later developed by Rumelhart and McClelland [2].following formula shown how weights and biases in BP algorithm will be updated.…”

Section: Fig1 Back Propagation Algorithmmentioning

confidence: 99%

Application Of Neural Network For Forecasting Gas Price In America

Sotoudeh¹,

Farshad²

2012

J. Math. Computer Sci.

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This paper presents a neuro-base approach for gas price forecasting of American consumers. in order to forming a neural network structure, effecting parameters on gas price are analyzed and gas production and consumption, import and export gas ,natural gas supplies held in storage,oil price are selected as inputs. this approach is structured as multi-level artificial neural net work(ANN)base on supervised muliti-layer perceptron (MLP),train with the levbergenberg-marquard algorithm .actual data from 1949-2010 is extracted from American energy information administration (EIA) .samples from 1949-2005 are used to train the multi-level ANN and the rest from 2005 to 2010 are used for network test. Result shows multi-level ANN is train well.

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“…The modern forecasting methods include artificial intelligence neural networks [9,10], chaotic time series methods [11], expert system forecasting methods [12], grey models [13,14], support vector Energies 2016, 9, 1050 3 of 30 machines [15,16], fuzzy systems [17], self-adaptable models [18], optimization algorithms and so on. The artificial neural networks (ANNs) can simulate the human brain to realize the intelligent dealing, and it can obtain a good forecasting performance when addressing the non-structural and non-linear time series data owing to their ability of self-adaptability, self-learning and memory.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Dong

et al. 2016

Energies

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Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people's livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

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A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran

Cited by 174 publications

References 18 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

Application Of Neural Network For Forecasting Gas Price In America

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

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