Modified neural and neuro-fuzzy approach for short term load forecasting

Chaturvedi, D. K.; Premdayal, Sinha Anand

doi:10.1109/icpces.2012.6508136

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The techniques used for the analysis of this random signal are: 1) Kalman Filters Method; 2) Box Jenkins Method; 3) Regression Processes; 4) Spectral Expansion Technique. [2,3,4]…”

Section: Time Series Methodsmentioning

confidence: 99%

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A Neuro-fuzzy Approach for Predicting Load Peak Profile

Abdellah

Djamel

2015

IJECE

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Load forecasting has many applications for power systems, including energy purchasing and generation, load switching, contract evaluation, and infrastructure development. Load forecasting is a complex mathematical process characterized by random data and a multitude of input variables.To solve load forecasting, two different approaches are used, the traditional and the intelligent one.Intelligent systems have proved their efficiency in load forecasting domain. Adaptive neuro-fuzzy inference systems (ANFIS) are a combination of two intelligent techniques where we can get neural networks and fuzzy logics advantages simultaneously. In this paper, we will forecast night load peak of Algerian power system using multivariate input adaptive neuro-fuzzy inference system (ANFIS) introducing the effect of the temperature and type of the day as input variables.

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“…The techniques used for the analysis of this random signal are: 1) Kalman Filters Method; 2) Box Jenkins Method; 3) Regression Processes; 4) Spectral Expansion Technique. [2,3,4]…”

Section: Time Series Methodsmentioning

confidence: 99%

“…The ANN can learnfrom experience, generalize from previous examples to newones, abstracts essential characteristics from input containing irrelevant data. The ANN gives more precise forecast as compared to conventional techniques [2].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%