AIMC Topic: Wind

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Multi-task learning for the prediction of wind power ramp events with deep neural networks.

Neural networks : the official journal of the International Neural Network Society Jan 7, 2020
In Machine Learning, the most common way to address a given problem is to optimize an error measure by training a single model to solve the desired task. However, sometimes it is possible to exploit latent information from other related tasks to impr...
Wind Deep Learning Energy-Generating Resources
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A novel compound wind speed forecasting model based on the back propagation neural network optimized by bat algorithm.

Environmental science and pollution research international Dec 28, 2019
Wind power, a clean and renewable resource, is regarded as one of the most promising and economical resources during the transformation from fossil fuels to new energy resources. Thus, the accuracy of wind speed forecasting work is very important to ...
Neural Networks, Computer Forecasting Wind Algorithms
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Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands.

The Science of the total environment Nov 20, 2019
Wetlands are extraordinary ecosystems and important climate regulators that also contribute to reduce natural disaster risk. Unfortunately, wetlands are declining much faster than forests. The safeguarding of the wetlands also needs knowledge of the ...
Wetlands Water Movements Models, Theoretical Environmental Monitoring Neural Networks, Computer Temperature Artificial Intelligence Wind
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Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance.

Neural networks : the official journal of the International Neural Network Society Sep 28, 2019
In this study, we use a deep convolutional neural network (CNN) to develop a model that predicts ozone concentrations 24 h in advance. We have evaluated the model for 21 continuous ambient monitoring stations (CAMS) across Texas. The inputs for the C...
Wind Deep Learning Time Factors Neural Networks, Computer Forecasting Air Pollution Ozone Meteorology Environmental Monitoring Air Pollutants Humans
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Prediction of environmental effects in received signal strength in FM/TV station based on meteorological parameters using artificial neural network and data mining.

Journal of environmental management Sep 9, 2019
In this paper, meteorological parameters, electric field strength and transmitters' output power measured during six months in a TV/FM station. There are 13 frequencies in FM and UHF frequency bands in pilot broadcast station. The analysis of results...
Meteorology Neural Networks, Computer Environmental Monitoring Wind Data Mining Linear Models
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Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking.

Plant physiology Jul 22, 2019
Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in m...
Motion Crops, Agricultural Environment Breeding Deep Learning Agriculture Triticum Phenotype Wind Algorithms
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Analysis of the role of wind information for efficient chemical plume tracing based on optogenetic silkworm moth behavior.

Bioinspiration & biomimetics May 31, 2019
Many animals use olfactory information to search for feeding areas and other individuals in real time and with high efficiency. We focus on the chemical plume tracing (CPT) ability of male silkworm moths and investigate an efficient CPT strategy for ...
Algorithms Odorants Male Moths Robotics Wind Flight, Animal Models, Biological Optogenetics Animals
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Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction.

PloS one May 31, 2019
Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimizat...
Humans Turkey Forecasting Solar Energy Wind Humidity Sunlight Support Vector Machine Algorithms Regression Analysis
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Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China.

Environmental pollution (Barking, Essex : 1987) May 27, 2019
Tropospheric ozone in the surface air has become the primary atmospheric pollutant in Hangzhou, China, in recent years. Previous analysis is not enough to decode it for better regulation. Therefore, we use the traditional atmospheric model, Weather R...
Weather Volatile Organic Compounds Meteorological Concepts Machine Learning Meteorology Forecasting Air Pollutants Neural Networks, Computer Wind Air Pollution China Ozone Environmental Monitoring
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Evolving connectionist systems (ECoSs): a new approach for modeling daily reference evapotranspiration (ET).

Environmental monitoring and assessment Aug 14, 2018
Over the last few years, the uses of artificial intelligence techniques (AI) for modeling daily reference evapotranspiration (ET) have become more popular and a considerable amount of models were successfully applied to the problem. Therefore, in the...
Algeria Wind Neural Networks, Computer Environmental Monitoring Artificial Intelligence Sunlight
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