Results 211 to 220 of about 3,413,140 (351)

Random forest machine learning algorithm based seasonal multi‐step ahead short‐term solar photovoltaic power output forecasting

IET Renewable Power Generation, EarlyView.
To maintain grid stability, the energy levels produced by sources within the network must be equal to the energy consumed by customers. In current times, achieving energy balance mainly involves regulating the electrical energy sources, as consumption is typically beyond the control of grid operators.
Sravankumar Jogunuri, Josh F.T, Albert Alexander Stonier, Geno Peter, Jayakumar Jayaraj, Jaganathan S, Jency Joseph J, Vivekananda Ganji   +7 more
wiley   +1 more source

Improved daily PM_2.5 estimates in India reveal inequalities in recent enhancement of air quality. [PDF]

Sci Adv
Kawano A, Kelp M, Qiu M, Singh K, Chaturvedi E, Dahiya S, Azevedo I, Burke M.   +7 more
europepmc   +1 more source

Spectral normalization generative adversarial networks for photovoltaic power scenario generation

IET Renewable Power Generation, EarlyView.
This paper integrates the spectral normalization method into the discriminator network to enhance the training stability of the network. Through simulations of centralized photovoltaic power station scenarios, the effectiveness of the selected methods is validated.
Xiurong Zhang, Shaoqian Fan, Daoliang Li
wiley   +1 more source

Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period. [PDF]

BMC Infect Dis
Zhu H, Chen S, Qin W, Aynur J, Chen Y, Wang X, Chen K, Xie Z, Li L, Liu Y, Chen G, Ou J, Zheng K.   +12 more
europepmc   +1 more source

Surrogate modelling for ice accretion prediction on wind turbine blades based on support vector regression

IET Renewable Power Generation, EarlyView.
Proposing a solution to the threat of ice accretion on wind turbine blades in extreme cold weather, this study introduces surrogate modelling using support vector regression for accurate prediction. The surrogate model, rigorously evaluated for reliability, demonstrates that real‐time pitch angle adjustments, informed by both the model and ...
Shouyuan Mao, Yangyang Wu, Liheng Zhao, Weike Mo   +3 more
wiley   +1 more source

An integrated prediction model based on meta ensemble learning for short‐term wind speed forecasting

IET Renewable Power Generation, EarlyView.
Considering the fluctuation and randomness of wind speed, a new integrated forecasting model is proposed for short‐term wind speed forecasting (WSF). The meta ensemble learning algorithm in this model can automatically adjust the coefficient of each base predictor to achieve optimal forecasting accuracy.
Zhengwei Ma, Ting Wu, Sensen Guo, Huaizhi Wang, Gang Xu, Saddam Aziz   +5 more
wiley   +1 more source

Modelling and Using Spatial Effects in Nationwide Historical Data Improve Genomic Prediction of Rice Heading Date in Japan. [PDF]

Rice (N Y)
Taniguchi S, Hayashi T, Nakagawa H, Matsushita K, Kajiya-Kanegae H, Yonemaru JI, Goto A.   +6 more
europepmc   +1 more source

Future climate change and the distributional shift of the common vampire bat, Desmodus rotundus. [PDF]

Sci Rep
Van de Vuurst P, Gohlke JM, Escobar LE.
europepmc   +1 more source

Understanding and predicting animal movements and distributions in the Anthropocene

Journal of Animal Ecology, EarlyView.
This review provides a framework to better understand and predict animal movements and distributions in human‐modified environments. Bridging the gap between movement models and management/conservation policy is challenging. Here, the recommendations presented highlight the exciting opportunities now available to advance the field of movement ecology ...
Sara Gomez, Holly M. English, Vanesa Bejarano Alegre, Paul G. Blackwell, Anna M. Bracken, Eloise Bray, Luke C. Evans, Jelaine L. Gan, W. James Grecian, Catherine Gutmann Roberts, Seth M. Harju, Pavla Hejcmanová, Lucie Lelotte, Benjamin Michael Marshall, Jason Matthiopoulos, AichiMkunde Josephat Mnenge, Bernardo Brandao Niebuhr, Zaida Ortega, Christopher J. Pollock, Jonathan R. Potts, Charlie J. G. Russell, Christian Rutz, Navinder J. Singh, Katherine F. Whyte, Luca Börger   +24 more
wiley   +1 more source

Extreme heat prediction through deep learning and explainable AI. [PDF]

PLoS One
Shafiq F, Zafar A, Ghani Khan MU, Iqbal S, Albesher AS, Asghar MN.   +5 more
europepmc   +1 more source