Short-term wind speed forecasting of knock airport based on ANN algorithms

Yadav, Mukhraj; Singh, Kumar Gaurav; Chaturvedi, Anurag

doi:10.1109/icomicon.2017.8279089

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…This is because ANN-BP is capable of being medicated by adding more neurons as a signal identifier or input data e.g., with n layer inputs, n layer hidden, and one layer output. In addition, this method has many activation functions and training algorithms to increase the percentage accuracy of the simulated data [11], [12], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Computational of Distribution of Wind Speed as Preliminary Information for Fishers: Case Study in Lombok Sea

2020

IJATCSE

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Wind speed and direction are two important things needed for safety by fishermen when going for fish in the sea. The shifting season in Indonesia is very visible nowadays, while the fishermen still use traditional techniques in determining the time will be at sea, so there is not a slight accident experienced by fishermen when it is in the middle of the ocean. Therefore, it is the responsibility of the relevant Government to convey early information to the fishermen for their safety. The purpose of this research is to provide alternative information in determining the wind speed and direction using the Artificial Neural Network type Back Propagation (ANN-BP) method. Prediction result with monthly data input for the last 12 years can be used as a reference by the Government to be presented to the fishermen in order to determine when to find fish and more anticipatory in seeing the speed of the wind is influential on the wave speed and height in the sea. The predicted result with the architecture of the 144 layers input, the 10 layers hidden-1, 5 layers hidden-2, and 1 output layer resulted in an average minimum wind speed of 2,996 knots, the average maximum wind speed of 31,500 knots, and the average wind direction of 207,824 0

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Section: Introductionmentioning

confidence: 99%

Computational of Distribution of Wind Speed as Preliminary Information for Fishers: Case Study in Lombok Sea

2020

IJATCSE

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“…1, pp. [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] This is an open access article published by the IET and Tianjin University under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) cloudless skies [11][12][13][14]. Recent research carried out shows that the mathematical models available in the literature are not accurate, primarily due to the extreme simplicity of parameterisation; however, empirical models based on multiple regression analysis are presented for estimating global solar energy.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, ANN-based models are introduced, employing artificial intelligent techniques which are data-driven and can subsequently perform the structure simulation. The ANN model is ideal for modelling non-linear, dynamic and complex system [38][39][40][41][42][43][44][45][46][47][48][49]. Chang et al proposed a radial basis function neural network (RBFNN) based model for short-term power forecasting wherein 24 h of input data at 10-min resolution have been considered for training the proposed neural network.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of intelligent modelling techniques for forecasting solar energy and its application in solar PV based energy system

Perveen¹,

Rizwan²,

Goel³

2019

IET Energy Systems Integration

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The measurement of solar energy data is a difficult task and rarely available even for those stations where measurement has already been done. Further, the PV power forecasting is an important element for smart energy management system. In the present scenario, utilities are developing the smart-grid application and PV power forecasting is an important key tool for a new paradigm. The forecasting of solar energy during clear sky-condition can be easily estimated using mathematical models; however, forecasting under the influence of hazy, cloudy, and foggy sky-conditions do not provide accuracy with these models. Therefore, an intelligent modelling techniques i.e. fuzzy logic, artificial neural network (ANN), and adaptive-neuralfuzzy-inference system (ANFIS) models are proposed based on sky-conditions namely clear/sunny sky, hazy sky, partially cloudy/foggy sky, and fully cloudy/foggy sky-conditions for forecasting global solar energy. To design the model, 15 years averaged datasets of meteorological parameters were used for distinct climate zones across India. Further, comparison of intelligent models has been carried out with regression models using statistical indicators. The proposed model has been implemented for short-term PV power forecasting under composite climatic conditions. Simulation results confirm that the ANFIS model provides supremacy for PV power forecast as compared to other models.

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Forecasting of solar radiation for a cleaner environment using robust machine learning techniques

Magesh

Parthiban

Kesavaraman

et al. 2022

Environ Sci Pollut Res

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An intensi ed research is going on worldwide to increase renewable energy sources like solar and wind to reduce emissions and achieve the worldwide targets and also to address the depleting fossil fuels resources and meet the increasing energy demand of the population. The Solar Radiation (SR) is intermittent, forecasting the solar radiation beforehand is a must. The objective of this research is to use Modern Machine Techniques for different climatic conditions to forecast SR with higher accuracy.The required dataset is collected from National Solar Radiation Database having features as temperature, pressure, relative humidity, dew point, solar zenith angle, wind speed and direction, with respect to the yparameter Global Horizontal Irradiance GHI (W/m 2 ). The collected data is rst split based on different types of climatic conditions. Each climatic model will be trained on various Machine Learning (ML) algorithms like Multiple Linear Regression(MLR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression(GBR), Lasso and Ridge Regression and Deep Learning Algorithm especially Long-short Term Memory (LSTM) using Google Colab Platform. From our analysis, LSTM has the least error approximation of 0.0040 loss at the 100th epoch and of all ML models, Gradient Boosting and RFR top high, when it comes to the Hot weather season -Gradient Boosting leads 2% than RFR and similarly for Cold weather, Autumn and monsoon climate -RFR has 1% higher accuracy than Gradient Boosting. This high accuracy model is deployed in a User Interface (UI) that will be more useful for real-time solar prediction, load operators for maintenance scheduling, stock commitment and load dispatch centers for engineers to decide on setting up solar panels, for household clients and future researchers.

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Short-term wind speed forecasting of knock airport based on ANN algorithms

Cited by 7 publications

References 16 publications

Computational of Distribution of Wind Speed as Preliminary Information for Fishers: Case Study in Lombok Sea

Computational of Distribution of Wind Speed as Preliminary Information for Fishers: Case Study in Lombok Sea

Comparison of intelligent modelling techniques for forecasting solar energy and its application in solar PV based energy system

Forecasting of solar radiation for a cleaner environment using robust machine learning techniques

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