Results 241 to 250 of about 137,935 (294)

Electric-load forecasting using interval models based on granularity and justifiable principles. [PDF]

Sci Rep
Al-Hmouz R, Mansouri M, Al-Badi A, Awad ASA, Hossen A.   +4 more
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Cyber-resilient machine learning framework for accurate individual load forecasting and anomaly detection in smart grids. [PDF]

Sci Rep
Tayseer M, Talaat M, Zamel AA, Sedhom BE, Elgamal M, Senjyu T, Song D, Ibrahim IM, Elkholy MH.   +8 more
europepmc   +1 more source

High-resolution energy data from a sustainable industrial production area in Karlsruhe. [PDF]

Sci Data
Sievers J, Bischof S, Blank T, Simon F.
europepmc   +1 more source

Demand forecasting and inventory optimization of distribution equipment: A fusion model based on genetic algorithm and machine learning. [PDF]

PLoS One
Tu Q, Zhang H, Li W, Duan J, Kong C.
europepmc   +1 more source

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Detection, mining and forecasting of impact load in power load forecasting

Applied Mathematics and Computation, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jianzhou Wang, Zhixin Ma, Lian Li
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A Fast and Stable Forecasting Model to Forecast Power Load

International Journal of Pattern Recognition and Artificial Intelligence, 2015
As the traditional gray forecasting model GM(1, 1) has poor performance in forecasting the fast-growing power load, we present a chaotic co-evolutionary particle swarm optimization (CCPSO) algorithm, one with better efficiency than the PSO algorithm. Based on the GM(1, 1) model, the CCPSO algorithm is adopted to solve the values of parameters a and b ...
Li-Zhi Tan, Aijia Ouyang, Xuyu Peng, Er-Xi Li, Tung Khac Truong, Libo Hao   +5 more
openaire   +1 more source

Power Load Forecasting Using a Refined LSTM

Proceedings of the 2019 11th International Conference on Machine Learning and Computing, 2019
The power load forecasting is based on historical energy consumption data of a region to forecast the power consumption of the region for a period of time in the future. Accurate forecasting can provide effective and reliable guidance for power construction and grid operation. This paper proposed a power load forecasting approach using a two LSTM (long-
Dedong Tang, Chen Li, Xiaohui Ji, Zhenyu Chen 0003, Fangchun Di   +4 more
openaire   +1 more source

Risk adjusted forecasting of electric power load

2014 American Control Conference, 2014
Load forecasting of energy demand is usually focused on mean values in related statistical models and ignores rare peak events. This paper provides Extreme Value Theory analysis of the peak events in electrical power load demand. It estimates risk of the peak events by combining forecast of the mean with extreme value modeling of distribution tail. The
Saahil Shenoy, Dimitry M. Gorinevsky
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A New Intelligent Method for Power Load Forecasting

Sixth International Conference on Intelligent Systems Design and Applications, 2006
Various factors that influence power load are more and more intricate. The traditional load forecasting methods can no longer adapt to the situation. Self-organizing method is a comparably new modeling method and so as to be easily used in the recognition and prediction of complex non-linear systems. Compared with traditional forecasting methods, it is
Wei Li, Renhui Huang, Jie Bai, Xiue Yuan, Dongxiao Niu   +4 more
openaire   +1 more source