Neural network and fuzzy logic statistical downscaling of atmospheric circulation-type specific weather pattern for rainfall forecasting

Valverde, María Cleofé; Araújo, Ernesto; Velho, Haroldo Campos

doi:10.1016/j.asoc.2014.02.025

Cited by 26 publications

(11 citation statements)

References 35 publications

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“…The strategies in the quantitative class incorporate numerical models, for example, moving average, straight-line projection and exponential smoothing. In the subjective classification subjective or intuitive models such as jury or executive opinion, sales force composite, and customer expectations is the strategy [15].…”

Section: Forecastin Gliteraturementioning

confidence: 99%

Hybrid Irradiation Forecasting Method Using Neural Network To Reduce Exponential Smoothing Error

Parsi¹

2016

IOSR

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Now a days talking about renewable energy is getting more common as long as researches are trying to come up with new ideas and updating previous approaches. One of the most common way to study and planning for increasing efficiency is forecasting demand and available resources. For forecasting different variables, different methods experimented. However, none of the approaches could minimize the forecasting error to the suitable point in renewable energy field such as, irradiation and wind prediction. In this paper one of the most frequent method for forecasting solar radiation were studied and examined which is exponential smoothing and the result of calculation were trained by neural network (feedforward). Neural network always used as an approach for forecasting separately but in this paper it is going to be used as part of the main calculation. Meaning to say that, neural network can be combined any method which in this case is exponential smoothing. The results was noticeable improved. Normally the average exponential smoothing errors would be around ten percent based on calculated results. But after applying neural network for completing the process, average error was declined to six percent.

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Section: Forecastin Gliteraturementioning

confidence: 99%

Hybrid Irradiation Forecasting Method Using Neural Network To Reduce Exponential Smoothing Error

Parsi¹

2016

IOSR

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“…To overcome the shortcomings of the ANN, various types of novel integrated intelligent networks have been developed, such as the FNNs [2,[18][19][20] and the wavelet neural networks (WNNs) [21]. WNN is a new branch of ANN theory research that is constructed by taking the wavelet transform as the preprocessor of the ANN.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Fuzzy Wavelet Neural Network Model with Self‐Adapted Fuzzy c‐Means Clustering and Genetic Algorithm for Water Quality Prediction in Rivers

et al. 2018

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Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network (FWNN) including the neural network (NN), the fuzzy logic (FL), the wavelet transform (WT), and the genetic algorithm (GA) was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network (FNN), the wavelet neural network (WNN), and the neural network (ANN). The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river.

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Section: Forecastingmentioning

confidence: 99%

“…Yet, results were surprising not as accurate as it was expected [7]. In this review paper, three most common used approaches for forecasting irradiation [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] were examined, which are exponential smoothing, seasonal method and neural network. For all these three same historical database were used and at the end comparison between them were designed.…”

mentioning

confidence: 99%

“…That's why, accurate forecasting of radiation is one of the challenges for scientists, not just only to predict, for designing as well. For time being, there are many approaches for forecasting irradiance [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. It has grown from mathematical formula and combination of them up to analyzing satellite images by using artificial intelligence.…”

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confidence: 99%

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Daily solar radiation forecasting using historical data and examining three methods

Parsi¹

2016

IOSR

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I IntroductionFilling up electronic demands using renewable energy is getting more interesting every day, not just only it is free to get, above of that because it is pollution free. Renewable energy sources as photovoltaic (PV), wind, bio-diesel generator are now utilized in integrated generation systems at sites that have a large potential of either solar, wind or both. At present, solar photovoltaic and wind systems have been promoted around the globe on a comparatively larger scale [1-6]. As it mentioned, these energies are free but doesn't mean that they are always available. For instants, solar photovoltaic energy system cannot be useful at night and also cannot be satisfied during cloudy days. It goes the same way for wind system, it cannot be efficient even during windy days, because amount of wind speed is required to get into that level, which makes the prediction harder for calculations. Even though if winds speed is satisfied doesn't mean that it will be constant even for an hour. Therefore, for saving energy to meet demands few of these system need to work together so they may cover each other up if it is possible for having a better reliability.Comparing to other efficient renewable energy system, solar is the most common cause, it is possible to use it daily, solar conversion, and renewable energy applications, particularly for the sizing of stand-alone photovoltaic (PV) systems [1]. Solar energy or radiation is not very easy to get as well, but comparing to others is more convenient, as it is known, in air there are a lot of particles which directly will effect on the solar radiation frequency that will effect on absorbing solar energy by PV's a good example would be clouds and haze. Complete sun radiation is the most essential alternative in forecasting of renewable energy systems, especially in designing PV power systems. Knowing a perfect sizing of solar system [2-4] (PV) is very important, by over-sizing the system, it doesn't mean that result will be better, because, by not considering prize and costs, by having a larger area, chances of having shaded area on PVs will increase which reduces efficiency of PVs performance quite a lot. In other hand, by having smaller size or in other word, under-sizing the system, reduces the power supply reliability [1]. That's why, accurate forecasting of radiation is one of the challenges for scientists, not just only to predict, for designing as well. For time being, there are many approaches for forecasting irradiance [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. It has grown from mathematical formula and combination of them up to analyzing satellite images by using artificial intelligence. Yet, results were surprising not as accurate as it was expected [7]. In this review paper, three most common used approaches for forecasting irradiation [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] were examined, which are exponential smoothing, seasonal method and neural network. For all these three same historical database were used an...

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Neural network and fuzzy logic statistical downscaling of atmospheric circulation-type specific weather pattern for rainfall forecasting

Cited by 26 publications

References 35 publications

Hybrid Irradiation Forecasting Method Using Neural Network To Reduce Exponential Smoothing Error

Hybrid Irradiation Forecasting Method Using Neural Network To Reduce Exponential Smoothing Error

A Hybrid Fuzzy Wavelet Neural Network Model with Self‐Adapted Fuzzy c‐Means Clustering and Genetic Algorithm for Water Quality Prediction in Rivers

Daily solar radiation forecasting using historical data and examining three methods

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