Results 211 to 220 of about 143,911 (316)

Predicting Solar Photovoltaic Power Output in Saudi Arabia's Jazan Region: Performance Comparison of Machine Learning Models

Energy Science &Engineering, EarlyView.
Workflow of the PV power estimation and ML forecasting methodology. ABSTRACT Accurate prediction of solar panel energy output is vital for managing power systems effectively and maintaining a stable electrical grid. This is especially important in regions that rely heavily on renewable sources. This research provides a direct comparison of five machine
Abdoalateef Alzhrani, Abdullahi Abubakar Mas'ud, Mohd Khairul Amri Kamarudin, Firdaus Muhammad‐Sukki, Abdullahi Mohammad   +4 more
wiley   +1 more source

Evaluation of LSTM-Based Short-Term Prediction of MBR Operational Parameters: Applicability and Limitations for Aeration Control Applications. [PDF]

Water Environ Res
Nguyen MB, Van Huynh V, Ueyama T, Hayashi M, Imai T, Miyazaki K, Itayama T.   +6 more
europepmc   +1 more source

Environmental Control for Edible Fungi Cultivation Based on Temporal Information and Deep Learning

Food Bioengineering, EarlyView.
ABSTRACT Currently, there are still prevalent issues in greenhouse environmental regulation, such as response lag, low control accuracy, and difficulty in coping with sudden environmental disturbances. To achieve high‐precision and dynamic control of the edible fungi cultivation environment, this study proposes an edible fungi environmental control ...
Xiangyan Wang, Shuangcheng Huang, Qingbo Lu, Xinyi Guo   +3 more
wiley   +1 more source

A Comparative Study of Multi-Scale Hybrid Deep Learning Frameworks for Estimation of Domestic Load Demand of Pakistan's Central Region. [PDF]

Sensors (Basel)
Bashir MY, Shakir M, Raza A, Ellahi M, Jamil M.   +4 more
europepmc   +1 more source

Triboelectric nanogenerators for vehicle intelligent cockpits

FlexMat, EarlyView.
Converting mechanical stimuli from drivers and vehicle motion into electrical signals, triboelectric nanogenerators provide promising interfaces toward passive, flexible, distributed and intelligent sensing for next‐generation automotive cockpits to support three core functional dimensions: driver state monitoring, vehicle state monitoring, and human ...
Haiqiu Tan, Shiquan Lü, Xiangwei Meng, Huiying Zhang, Wenxing Zhang, Dongxian Sun, Xiao Lu, Lei Wang, Haodong Zhang, William W. Yu   +9 more
wiley   +1 more source

A deep learning-based model for interval prediction of real-time clearing price in electricity market. [PDF]

iScience
Yang J, Zhou J, Zhu L.
europepmc   +1 more source

Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models

Journal of Forecasting, EarlyView.
ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly, Pooja Sengupta, Divya Aggarwal   +2 more
wiley   +1 more source

Energy Consumption and Carbon Emission Prediction of District Heating System in Residential Communities Based on SSA-LSTM Model. [PDF]

Sensors (Basel)
Zhao B, Xu L, Zheng Z, Wu Y, Yuan T.
europepmc   +1 more source

LSTM-H: A hybrid deep learning model for accurate livestock movement prediction in UAV-based monitoring systems

Accurately predicting livestock movement is a cornerstone of precision agriculture, enabling smarter resource management, improved animal welfare, and enhanced productivity.
Ayub Bokani (9781808), Elaheh Yadegaridehkordi (17151835), SS Kanhere (9863402)   +2 more
core  

A Novel Text‐Based Framework for Forecasting Carbon Prices

Journal of Forecasting, EarlyView.
ABSTRACT This study proposes a text‐based framework for predicting EU carbon prices. Using weekly data from 2020 to 2024, we construct a multivariate dataset combining financial indicators, commodity prices, Google Trends measures, and news‐based sentiment extracted using FinBERT.
Christian Oliver Ewald, Yaoyu Li
wiley   +1 more source