Results 41 to 50 of about 453,955 (199)

Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning [PDF]

, 2014
A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and
Kurbatsky, Victor, Leahy, Paul, Sidorov, Denis, Spiryaev, Vadim, Tomin, Nikita, Zhukov, Alexei   +5 more
core   +2 more sources

Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

, 2017
Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions.
Chen, Ke, Jintao, Xiqun, Yang, Hai, Zheng, Hongyu   +4 more
core   +1 more source

Short Term Load Forecasting Based Artificial Neural Network [PDF]

Iraqi Journal for Electrical and Electronic Engineering, 2014
Present study develops short term electric load forecasting using neural network; based on historical series of power demand the neural network chosen for this network is feed forward network, this neural network has five input variables ( hour of the ...
Adel M. Dakhil
doaj  

Short-term Load Forecasting Based On Variational Mode Decomposition And Chaotic Grey Wolf Optimization Improved Random Forest Algorithm

Journal of Applied Science and Engineering, 2022
To enrich short-term load forecasting methods and improve forecasting accuracy, a short-term load forecasting method based on variational mode decomposition and chaotic grey wolf optimization (CGWO) improved random forest (RF) is proposed.
Fan Wang, Chen Chen, Haitao Zhang, Youhua Ma   +3 more
doaj   +1 more source

Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks

, 2018
We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced ...
Byeon, Wonmin, Koumoutsakos, Petros, Sapsis, Themistoklis P., Vlachas, Pantelis R., Wan, Zhong Y.   +4 more
core   +1 more source

Short-Term Load Forecasting Based on Complementary Ensemble Empirical Mode Decomposition and Long Short-Term Memory

Zhongguo dianli, 2020
With the continuous development of power industry, the importance of load forecasting is becoming more and more obvious. As an important part of load forecasting, short-term load forecasting is of great significance to the dispatching and operation of ...
Huiru ZHAO, Yihang ZHAO, Sen GUO
doaj   +1 more source

Individualized Short-Term Electric Load Forecasting Using Data-Driven Meta-Heuristic Method Based on LSTM Network

Sensors, 2022
Short-term load forecasting is viewed as one promising technology for demand prediction under the most critical inputs for the promising arrangement of power plant units.
Lichao Sun, Hang Qin, Krzysztof Przystupa, Michal Majka, Orest Kochan   +4 more
doaj   +1 more source

MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses [PDF]

, 2018
Recent approaches on trajectory forecasting use tracklets to predict the future positions of pedestrians exploiting Long Short Term Memory (LSTM) architectures.
Cristani, Marco, Del Bue, Alessio, Galasso, Fabio, Hasan, Irtiza, Setti, Francesco, Tsesmelis, Theodore   +5 more
core   +2 more sources

Short- and Very Short-Term Firm-Level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

Energies, 2022
Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type which remain under-researched in the demand-side energy forecasting literature.
Andrea Maria N. C. Ribeiro, Pedro Rafael X. do Carmo, Patricia Takako Endo, Pierangelo Rosati, Theo Lynn   +4 more
doaj   +1 more source

Power load forecasting [PDF]

, 2006
For the electric power factory, the power load forecasting problem, including load forecasting and consumption predicting, is crucial to work planning. According to the predicting time, it can be divided into long-term forecasting, mid-term forecasting ...
Hao, Z., Lu, L., Shao, D.
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