Load Forecasting Model Based on Amendment of Mamdani Fuzzy System

Yang, Kuihe; Zhao, Lian

doi:10.1109/wicom.2009.5301638

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Yang et al [72] proposed a neural network based short term load forecast model with fuzzy logic. Fuzzy logic membership function is designed in such a way that, they will select most influencing inputs of forecast model.…”

Section: Ann With Fuzzy Logic and Genetic Algorithmmentioning

confidence: 99%

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Raza

Khosravi

2015

Renewable and Sustainable Energy Reviews

769

286

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Section: Ann With Fuzzy Logic and Genetic Algorithmmentioning

confidence: 99%

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Raza

Khosravi

2015

Renewable and Sustainable Energy Reviews

769

286

View full text Add to dashboard Cite

“…Regarding load demand prediction, an ANN-based fuzzy logic imports a predictive model ANN to classify a large input data set has been developed. Yang and Zhao (2009) presented a technique that focuses on reducing the complexity of the system structure and improving predictive performance. Based on the results, load forecasting might be increased to a certain extent.…”

Section: Ann and Fuzzy Logicmentioning

confidence: 99%

Survey of computational intelligence as basis to big flood management: challenges, research directions and future work

Fotovatikhah¹,

Herrera

Shamshirband

et al. 2018

Engineering Applications of Computational Fluid Mechanics

239

139

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Flooding produces debris and waste including liquids, dead animal bodies and hazardous materials such as hospital waste. Debris causes serious threats to people's health and can even block the roads used to give emergency aid, worsening the situation. To cope with these issues, flood management systems (FMSs) are adopted for the decision-making process of critical situations. Nowadays, conventional artificial intelligence and computational intelligence (CI) methods are applied to early flood event detection, having a low false alarm rate. City authorities can then provide quick and efficient response in post-disaster scenarios. This paper aims to present a comprehensive survey about the application of CI-based methods in FMSs. CI approaches are categorized as single and hybrid methods. The paper also identifies and introduces the most promising approaches nowadays with respect to the accuracy and error rate for flood debris forecasting and management. Ensemble CI approaches are shown to be highly efficient for flood prediction.

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“…We combine the inputs which are ambiguous by fuzzy rules in order to find firing strength of a rule as output nodes. Every node in this layer is considered as a fixed node labeled ∏, the output is the multiplication of the incoming signal, here we modify these rules in order to reduce the error [14,15] 2 i Q = Wi = µAi(x) µBi(y), i=1,2,3…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Short Term Load Forecasting based on An Optimized Architecture of Hybrid Neural Network Model

2017

IJIET

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Nowadays the predication of electricity demand is consdired as a main criteria for estimating the electricity generation costs. Companies always sought to create balancing between the supply and demand of power. We are interested in this paper is short-term load forecasting (1 hour -1 week). The important factors that are considered, in this paper, for the electricity supply are the temperatures and time. Then, different neural network models (NN) are used and developped for increasing the accuracy of the short term load forecasting such as backpropagation, Adaptive Neuro-Fuzzy Inference System (ANFIS), wherease Wavelets Neural Network (WNN) is used as a comparative model. An optimized neural network is found by defining the suitable number of hidden layers and their nodes and an optimized membership functions number associated to each input unit is proposed in ANFIS model. A comparison between these models shows that the optimised ANFIS model outperfroms the other methods.

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Load Forecasting Model Based on Amendment of Mamdani Fuzzy System

Cited by 4 publications

References 11 publications

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Survey of computational intelligence as basis to big flood management: challenges, research directions and future work

Short Term Load Forecasting based on An Optimized Architecture of Hybrid Neural Network Model

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