Machine learning approaches to predict electricity production from renewable energy sources
Bearing in mind European Green Deal assumptions regarding a significant reduction of
green house emissions, electricity generation from Renewable Energy Sources (RES) is …
green house emissions, electricity generation from Renewable Energy Sources (RES) is …
A selective review on recent advancements in long, short and ultra-short-term wind power prediction
With large penetration of wind power into power grids, the accurate prediction of wind power
generation is becoming extremely important. Planning, scheduling, maintenance, trading …
generation is becoming extremely important. Planning, scheduling, maintenance, trading …
Ensemble machine learning for modeling greenhouse gas emissions at different time scales from irrigated paddy fields
Quantifying greenhouse gas (GHG) emissions from irrigated paddy fields is of great
significance for addressing climate change. Machine learning (ML) provides an alternative …
significance for addressing climate change. Machine learning (ML) provides an alternative …
Ultra short-term wind power forecasting based on sparrow search algorithm optimization deep extreme learning machine
G An, Z Jiang, L Chen, X Cao, Z Li, Y Zhao, H Sun - Sustainability, 2021 - mdpi.com
Improving the accuracy of wind power forecasting is an important measure to deal with the
uncertainty and volatility of wind power. Wind speed and wind direction are the most …
uncertainty and volatility of wind power. Wind speed and wind direction are the most …
Can ensemble machine learning be used to predict the groundwater level dynamics of farmland under future climate: a 10-year study on Huaibei Plain
Z Jiang, S Yang, Z Liu, Y Xu, T Shen, S Qi… - … Science and Pollution …, 2022 - Springer
Accurate and simple prediction of farmland groundwater level (GWL) is an important aspect
of agricultural water management. A farmland GWL prediction model, GWPRE, was …
of agricultural water management. A farmland GWL prediction model, GWPRE, was …
Adoption and realization of deep learning in network traffic anomaly detection device design
G Wei, Z Wang - Soft Computing, 2021 - Springer
In order to study the application of deep learning in the design of network traffic anomaly
detection device, aiming at two common problems in the field of network anomaly detection …
detection device, aiming at two common problems in the field of network anomaly detection …
A wind power prediction method based on DE-BP neural network
With the continuous increase of installed capacity of wind power, the influence of large-scale
wind power integration on the power grid is becoming increasingly apparent. Ultra-short …
wind power integration on the power grid is becoming increasingly apparent. Ultra-short …
A novel ultra-short-term wind power prediction model jointly driven by multiple algorithm optimization and adaptive selection
Ultrashort-term wind power forecasting with great precision and robustness is essential for
improving power quality and reliability management and reducing the cost of rotating …
improving power quality and reliability management and reducing the cost of rotating …
Short-term prediction of wind power using an improved kernel based optimized deep belief network
The unpredictability of weather condition leads to substantial inaccuracies in wind power
prediction, which not only increases the expenses for power reserve management but also …
prediction, which not only increases the expenses for power reserve management but also …
Short-term wind power prediction based on data reconstruction and improved extreme learning machine
H Li, H Zou - Arabian Journal for Science and Engineering, 2022 - Springer
With the purpose of improving the accuracy of the wind power short-term forecasting in an
effective way, improved wavelet threshold denoising and principal component analysis …
effective way, improved wavelet threshold denoising and principal component analysis …