Neuro-Fuzzy Approach Based Short Term Electric Load Forecastig

Chauhan, Bhavesh Kumar; Sharma, Amit; Hanmandlu, M.

doi:10.1109/tdc.2005.1546881

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…This important attribute of the RNN offers a tremendously significant advantage, especially in real-time applications. RNN can have an unrestricted memory level and can therefore learn connections through time in addition to learning via all current possible inputs [22]. The RNN is depicted in Fig.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Artificial Neural Network Models for Rainfall Prediction

Dada

Yakubu

Oyewola

2021

EJECE

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Rainfall prediction is an important meteorological problem that can greatly affect humanity in areas such as agriculture production, flooding, drought, and sustainable management of water resources. The dynamic and nonlinear nature of the climatic conditions have made it impossible for traditional techniques to yield satisfactory accuracy for rainfall prediction. As a result of the sophistication of climatic processes that produced rainfall, using quantitative techniques to predict rainfall is a very cumbersome task. The paper proposed four non-linear techniques such as Artificial Neural Networks (ANN) for rainfall prediction. ANN has the capacity to map different input and output patterns. The Feed Forward Neural Network (FFNN), Cascade Forward Neural Network (CFNN), Recurrent Neural Network (RNN), and Elman Neural Network (ENN) were used to predict rainfall. The dataset used for this work contains some meteorological variables such as temperature, wind speed, humidity, rainfall, visibility, and others for the year 2015-2019. Simulation results indicated that of all the proposed Neural Network (NN) models, the Elman NN model produced the best performance. We also found out that Elman NN has the best performance for the year 2018 (having the lowest RMSE, MSE, and MAE of 6.360, 40.45, and 0.54 respectively). The results indicated that NN algorithms are robust, dependable, and reliable algorithms that can be used for daily, monthly, or yearly rainfall prediction.

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Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Artificial Neural Network Models for Rainfall Prediction

Dada

Yakubu

Oyewola

2021

EJECE

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“…Iparticular in real time applications, the fundamental characteristic of RNN offers an extremely large advantage. In adding to learning via all current inputs, RNN can have an unbounded memory level, and can thus learn connections over time [19]. The RNN is showed in Fig.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Financial derivative features based integrated potential fishing zone (IPFZ) Future forecast

Raja

Kumar

2023

IFS

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In India, around 7 million people depend on fishing for their livelihoods. They are assisted with a reliable and fast brief forecast for the areas of fish aggregations. Habitat mapping is critical in supporting strategic choices on fish usage and protection. In conjunction with techniques for machine learning, remote control has made comprehensive fish mapping on relevant scales possible. In machine learning, supervised algorithms are utilized to make forecasts from datasets, when data is accessible without relating output factors. In this research work, Ocean Surface Temperature (OST) and Satellite derived Chlorophyl material are the basic inputs to generating the information of Potential Fishing Zone (PFZ). The 16 features and additional financial derivative features are used for accurate future prediction of PFZ. The unwanted and missing data are removed using effective pre-processing techniques. Among the various methods available for forecasting nonlinear phenomena, the Neural Network is the best and the efficient method to get a forecast. Therefore, the Function Fitting Neural Network (FFNN) technique is mainly used to predicting the Integrated Potential Fishing Zone (IPFZ). The practical analyses are performed by analysing the 80% -20%, 60% -40% and future data in terms of various parameters. From the results, it is proved that the suggested FFNN achieved 90% of accuracy, where the existing neural network achieved 86% of accuracy by implementing with financial derivative features for the 80% -20% of available dataset.

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“…In particular in real time applications, the fundamental characteristic of RNN offers an extremely large advantage. In adding to learning via all current inputs, RNN can have an unbounded memory level, and can thus learn connections over time (B. K. Chauhan et al, 2005). The RNN is showed in Figure 7.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Financial Derivative Features based Integrated Potential Fishing Zone (IPFZ) Future Forecast

Raja¹,

Kumar²,

Doss³

2022

Preprint

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In India, around 7 million people depend on fishing for their livelihoods. They are assisted with a reliable and fast brief forecast for the areas of fish aggregations. Habitat mapping is critical in supporting strategic choices on fish usage and protection. In conjunction with techniques for machine learning, remote control has made comprehensive fish mapping on relevant scales possible. In machine learning, supervised algorithms are utilized to make forecasts from datasets, when data is accessible without relating output factors. In this research work, Ocean Surface Temperature (OST) and Satellite derived Chlorophyl material are the basic inputs to generating the information of PFZ. The 16 features and additional financial derivative features are used for accurate future prediction of PFZ. The unwanted and missing data are removed using effective pre-processing techniques. Among the various methods available for forecasting nonlinear phenomena, the Neural Network is the best and the efficient method to get a forecast. Therefore, the Function Fitting Neural Network (FFNN) technique is mainly used to predicting the IPFZ. The practical analysis is performed by analysing the 80%-20%, 60%-40% and future data in terms of various parameters. From the results, it is proved that the suggested FFNN achieved 90% of accuracy, where the existing neural network achieved 86% of accuracy by implementing with financial derivative features for the 80%-20% of available dataset.

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Neuro-Fuzzy Approach Based Short Term Electric Load Forecastig

Cited by 10 publications

References 27 publications

Artificial Neural Network Models for Rainfall Prediction

Artificial Neural Network Models for Rainfall Prediction

Financial derivative features based integrated potential fishing zone (IPFZ) Future forecast

Financial Derivative Features based Integrated Potential Fishing Zone (IPFZ) Future Forecast

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