Short term load forecasting for Iran National Power System and its regions using multi layer perceptron and fuzzy inference systems

Barzamini, R.; Menhaj, Mohammad Bagher; Khosravi, Abbas; Kamalvand, Sh.

doi:10.1109/ijcnn.2005.1556316

Cited by 13 publications

(10 citation statements)

References 14 publications

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“…On the contrary, artificial intelligent based techniques, and in particular neural networks (NNs), possess an excellent capability of learning and approximating nonlinear relationships to any arbitrary degree of accuracy (universal approximators) [7]. Applications of expert systems [8], [9], NNs [10]- [12] (and references therein), fuzzy systems [13], and neuro-fuzzy systems [14] have proliferated for STLF within the last two decades. It has been also stated that the majority of commercial STLF packages used by utility companies have been developed based on artificial intelligent-based techniques (mainly NNs) [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Construction of Optimal Prediction Intervals for Load Forecasting Problems

Khosravi

Nahavandi

Creighton

2010

IEEE Trans. Power Syst.

225

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Section: Introductionmentioning

confidence: 99%

Construction of Optimal Prediction Intervals for Load Forecasting Problems

Khosravi

Nahavandi

Creighton

2010

IEEE Trans. Power Syst.

225

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“…This gives a very unique property of CGP that each and every node in CGP might not be contributing to the overall evolved circuitry. CGP has been used to develop many efficient applications 30 , 31 , 32 .…”

Section: Cartesian Genetic Programming (Cgp)mentioning

confidence: 99%

Electricity Peak Load Forecasting using CGP based Neuro Evolutionary Techniques

Khan¹,

Arshad²

2016

IJCIS

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Proficient Economic and Financial planning is critical to the successful and efficient operation of power generating and distributing units. This planning becomes quite facile if the accurate and precise knowledge regarding the required power load is ascertained. This research is an innovative effort to bring forward different electric peak load forecasting models based on the Neuroevolutionary technique known as the Cartesian Genetic Programming Evolved Artificial Neural Network(CGPANN). Although CGPANN in itself is not novel but its application to power load forecasting is quite unique because of its innate ability to identify the best computationally efficient predictive model along with the recognition of the best appropriate features for load forecasting. Both the Feedforward and Recurrent CGPANN setups have been evolved here for peak load forecasting on a daily basis. The different setups developed have been trained and tested on the peak load data of United Kingdom National Grid. The models developed in this research have been tested and compared against previously proposed machine learning models. In comparison to contemporary models in the field, a more efficient and accurate peak load forecasting model has been produced using CGPANN based prediction methods.

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“…The techniques are mostly based on statistical and time-series analysis [1], learning algorithms, or expert systems [2]. Neural networks (NNs) [3][4][5][6][7], fuzzy expert systems [8][9][10], wavelet-based networks [11][12][13], or a combination of these methods [14][15][16][17] are examples of expert systems that have been investigated for short-term prediction in the literature.…”

Section: Introductionmentioning

confidence: 99%

Short-term load forecasting using mixed lazy learning method

Barakati¹,

Gharaveisi²,

Rafiei³

2015

Turk J Elec Eng & Comp Sci

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Abstract:A novel short-term load forecasting method based on the lazy learning (LL) algorithm is proposed. The LL algorithm's input data are electrical load information, daily electricity consumption patterns, and temperatures in a specified region. In order to verify the ability of the proposed method, a load forecasting problem, using the Pennsylvania-New Jersey-Maryland Interconnection electrical load data, is carried out. Three LL models are proposed: constant, linear, and mixed models. First, the performances of the 3 developed models are compared using the root mean square error technique. The best technique is then selected to compete with the state-of-the-art neural network (NN) load forecasting models. A comparison is made between the performances of the proposed mixed-model LL as the superior LL model and the radial basis function and multilayer perceptron NN models. The results reveal significant improvements in the precision and efficiency of the proposed forecasting model when compared with the NN techniques.

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Short term load forecasting for Iran National Power System and its regions using multi layer perceptron and fuzzy inference systems

Cited by 13 publications

References 14 publications

Construction of Optimal Prediction Intervals for Load Forecasting Problems

Construction of Optimal Prediction Intervals for Load Forecasting Problems

Electricity Peak Load Forecasting using CGP based Neuro Evolutionary Techniques

Short-term load forecasting using mixed lazy learning method

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