Application of Artificial Neural Network to Improve Quantitative Precipitation Forecasts of Meso-scale Numerical Weather Prediction

Prakiraan hujan bulanan bisa digunakan untuk antisipasi banjir dan manajemen sumber daya air, keselamatan jiwa dan harta benda, serta keberlangsungan aktivitas ekonomi. Penggunaan Jaringan Syaraf Tiruan sebagai bagian dari Machine Learning adalah teknik yang sering digunakan selain numerical weather prediction dan metode statistik. Menggunakan peru-bah data bulan dan data empirical orthogonal function anomali suhu muka laut bulanan pada 12 lokasi menghasilkan nilai korelasi yang baik saat pembuatan model, tetapi hasil verifikasi menunjukkan akurasi yang baik didapatkan saat periode musim kemarau dan skill terjelek saat peralihan musim kemarau ke musim hujan.

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Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Energies ◽

10.3390/en14020338 ◽

2021 ◽

Vol 14 (2) ◽

pp. 338

Author(s):

Lorenzo Donadio ◽

Jiannong Fang ◽

Fernando Porté-Agel

Keyword(s):

Machine Learning ◽

Wind Energy ◽

Wind Power ◽

Numerical Weather Prediction ◽

Wind Farm ◽

Weather Prediction ◽

Wind Power Forecasting ◽

Numerical Weather ◽

Artificial Neural ◽

Power Forecasting

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

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Rainfall Forecasting Using Various Artificial Neural Network Techniques - A Review

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2173159 ◽

2021 ◽

pp. 506-526

Author(s):

Nisha Thakur ◽

Sanjeev Karmakar ◽

Sunita Soni

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Moving Average ◽

Weather Prediction ◽

Optimization Technique ◽

Back Propagation ◽

Rainfall Forecasting ◽

Rainfall Prediction ◽

Artificial Neural ◽

Review Reports

The present review reports the work done by the various authors towards rainfall forecasting using the different techniques within Artificial Neural Network concepts. Back-Propagation, Auto-Regressive Moving Average (ARIMA), ANN , K- Nearest Neighbourhood (K-NN), Hybrid model (Wavelet-ANN), Hybrid Wavelet-NARX model, Rainfall-runoff models, (Two-stage optimization technique), Adaptive Basis Function Neural Network (ABFNN), Multilayer perceptron, etc., algorithms/technologies were reviewed. A tabular representation was used to compare the above-mentioned technologies for rainfall predictions. In most of the articles, training and testing, accuracy was found more than 95%. The rainfall prediction done using the ANN techniques was found much superior to the other techniques like Numerical Weather Prediction (NWP) and Statistical Method because of the non-linear and complex physical conditions affecting the occurrence of rainfall.

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