Zobrazeno 1 - 10
of 1 454
pro vyhledávání: '"Short-Term Forecasting"'
Publikováno v:
Energy Science & Engineering, Vol 12, Iss 11, Pp 5045-5060 (2024)
ABSTRACT Accurate ultra‐short‐term wind power prediction techniques are crucial for ensuring the efficient and safe operation of wind farms and power systems. Combined models based on data decomposition‐prediction techniques have shown excellen
Externí odkaz:
https://doaj.org/article/e7125d9b646547c6897abb8921479cae
Publikováno v:
Известия высших учебных заведений: Проблемы энергетики, Vol 26, Iss 4, Pp 75-88 (2024)
RELEVANCE of the study lies in the development of system for the short-term forecasting of power consumption by the enterprise of the oil and gas industry with consideration of technological factors and interpretation of their influence on the result
Externí odkaz:
https://doaj.org/article/8fb9f8e41e00417ea8d67c7f9b2dd881
Autor:
Robert M. X. Wu, Niusha Shafiabady, Huan Zhang, Haiyan Lu, Ergun Gide, Jinrong Liu, Clement Franck Benoit Charbonnier
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-18 (2024)
Abstract This research aims to explore more efficient machine learning (ML) algorithms with better performance for short-term forecasting. Up-to-date literature shows a lack of research on selecting practical ML algorithms for short-term forecasting
Externí odkaz:
https://doaj.org/article/feb2fb15481d4eb7889075c3146c295a
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
Photovoltaic (PV) power is greatly uncertain due to the random meteorological parameters. Therefore, accurate PV power forecasting results are significant for the dispatching of power and improving of system stability. This paper proposes a hybrid fo
Externí odkaz:
https://doaj.org/article/f39e762da7f444d09a2e360bb69401ef
Autor:
Lipika Goel, Neha Nandal, Sonam Gupta, Madhavi Karanam, Lakshmi Prasanna Yeluri, Alok Kumar Pandey, Oleg Igorevich Rozhdestvenskiy, Pyotr Grabovy
Publikováno v:
Cogent Engineering, Vol 11, Iss 1 (2024)
Demand forecasting, a crucial aspect of anticipating future customer needs, involves using historical data to predict trends. With the rise of artificial intelligence (AI), companies are increasingly turning to machine learning algorithms to enhance
Externí odkaz:
https://doaj.org/article/90dae623ce64491da05be304e13d6333
Publikováno v:
International Journal of Electrical Power & Energy Systems, Vol 158, Iss , Pp 109947- (2024)
Industrial activities are transitioning towards decarbonization, focusing on renewable energy sources, particularly photovoltaic solar energy. However, the inherent high variability of photovoltaic energy poses challenges. Some of them can be partial
Externí odkaz:
https://doaj.org/article/459c48c5bec24fc68081a4b548e768c1
Publikováno v:
Energy Reports, Vol 10, Iss , Pp 1387-1408 (2023)
The importance of accurate forecasting in the electric sector has grown due to the increasing demand and adoption of high volume of Renewable Energy Sources (RES). Short-term forecasting (STF) using deep learning methods has shown potential for impro
Externí odkaz:
https://doaj.org/article/09c5ce6fd7de4f658ad4761b63cd9a93
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract In this paper, NeuralProphet (NP), an explainable hybrid modular framework, enhances the forecasting performance of pandemics by adding two neural network modules; auto-regressor (AR) and lagged-regressor (LR). An advanced deep auto-regresso
Externí odkaz:
https://doaj.org/article/a39f568617be43babd65dcbd9df6c865
Short-Term Forecasting of Convective Weather Affecting Civil Aviation Operations Using Deep Learning
Publikováno v:
IEEE Access, Vol 12, Pp 166011-166030 (2024)
With the rapid development of the civil aviation industry, flight delays caused by convective weather are becoming increasingly severe. In terminal airspace with complex traffic environments, these delays can propagate to subsequent arrivals and depa
Externí odkaz:
https://doaj.org/article/4bb22d7548224c49b8fa07cdba71679f
Publikováno v:
IEEE Access, Vol 12, Pp 63361-63380 (2024)
As a result of climate change, the difficulty in the prediction of short-term rainfall amounts has become a necessary area of research. The existing numerical weather prediction models have limitations in precipitation forecasting especially due to h
Externí odkaz:
https://doaj.org/article/4a9472e24e3a4728839881bdd9af4563