Influence of Wind Speed on Solar PV Plant Power Production—Prediction Model Using Decision-Based Artificial Neural Network

2020 ◽  
pp. 3-16
Author(s):  
Roshan Mohanty ◽  
Paresh G. Kale
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Prediction Model ◽  
Power Production ◽  
Solar Pv ◽  
Artificial Neural ◽  
Plant Power ◽  
Production Prediction

scholarly journals Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network

Energies ◽  
2017 ◽  
Vol 10 (11) ◽  
pp. 1744 ◽  
Author(s):  
Athraa Ali Kadhem ◽  
Noor Wahab ◽  
Ishak Aris ◽  
Jasronita Jasni ◽  
Ahmed Abdalla
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Weibull Distribution ◽  
Prediction Model ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction ◽  
Artificial Neural

scholarly journals Development of Artificial Neural Network Model for Estimation of Salt Fields Productivity

2019 ◽  
Vol 20 (2) ◽  
pp. 152
Author(s):  
Indra Cahyadi ◽  
Heri Awalul Ilhamsah ◽  
Ika Deefi Anna
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Prediction Model ◽  
Factorial Design ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Model Performance ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In recent years, Indonesia needs import million tons of salt to satisfy domestic industries demand. The production of salt in Indonesia is highly dependent on the weather. Therefore, this article aims to develop a prediction model by examining rainfall, humidity and wind speed data to estimate salt production. In this research, Artificial Neural Network (ANN) method is used to develop a model based on data collected from Kaliumenet Sumenep Madura.  The model analysis uses the full experimental factorial design to determine the effect of the ANN parameter differences. Then, the selected model performance compared with the estimate predictor of Holt-Winters. The results present that ANN-based models are more accurate and efficient for predicting salt field productivity.

scholarly journals Development of Artificial Neural Network Model for Estimation of Salt Fields Productivity

2019 ◽  
Vol 20 (2) ◽  
pp. 48
Author(s):  
Indra Cahyadi ◽  
Heri Awalul Ilhamsah ◽  
Ika Deefi Anna
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Prediction Model ◽  
Factorial Design ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Model Performance ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In recent years, Indonesia needs import million tons of salt to satisfy domestic industries demand. The production of salt in Indonesia is highly dependent on the weather. Therefore, this article aims to develop a prediction model by examining rainfall, humidity and wind speed data to estimate salt production. In this research, Artificial Neural Network (ANN) method is used to develop a model based on data collected from Kaliumenet Sumenep Madura.  The model analysis uses the full experimental factorial design to determine the effect of the ANN parameter differences. Then, the selected model performance compared with the estimate predictor of Holt-Winters. The results present that ANN-based models are more accurate and efficient for predicting salt field productivity.

scholarly journals A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3350 ◽  
Author(s):  
Kittipong Kasantikul ◽  
Dongkai Yang ◽  
Qiang Wang ◽  
Aung Lwin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Particle Filter ◽  
Maritime Transportation ◽  
Global Navigation Satellite Systems ◽  
Speed Estimation ◽  
Estimation Model ◽  
Main Concept ◽  
Artificial Neural

Oceanographic remote sensing, which is based on the sensitivity of reflected signals from the Global Navigation Satellite Systems (GNSS), so-called GNSS-Reflectometry (GNSS-R), is very useful for the observation of ocean wind speed. Wind speed estimation over the ocean is the core factor in maritime transportation management and the study of climate change. The main concept of the GNSS-R technique is using the different times between the reflected and the direct signals to measure the wind speed and wind direction. Accordingly, this research proposes a novel technique for wind speed estimation involving the integration of an artificial neural network and the particle filter based on a theoretical model. Moreover, particle swarm optimization was applied to find the optimal weight and bias of the artificial neural network, in order to improve the accuracy of the estimation result. The observation dataset of the reflected signal information from BeiDou Geostationary Earth Orbit (GEO) satellite number 4 was used as an input for the estimation model. The data consisted of two phases with I and Q components. Two periods of BeiDou data were selected, the first period was from 3 to 8 August 2013 and the second period was from 12 to 14 August 2013, which corresponded to events from the typhoon Utor. The in situ wind speed measurement collected from the buoy station was used to validate the results. A coastal experiment was conducted at the Yangjiang site located in the South China Sea. The results show the ability of the proposed technique to estimate wind speed with a root mean square error of approximately 1.9 m/s.

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