Zobrazeno 1 - 10
of 653
pro vyhledávání: '"Electric load forecasting"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract This research work focuses on addressing the challenges of electric load forecasting through the combination of Support Vector Regression and Long Short-Term Memory (SVR/LSTM) methodology. The model has been modified by a flexible version of
Externí odkaz:
https://doaj.org/article/af41bff86a754c3ea63a9e657b8d8b9e
Publikováno v:
International Journal of Energy Economics and Policy, Vol 14, Iss 5 (2024)
To transform Mexico's electric load infrastructure, accurate electric load forecasts are required that are crucial to efficiently allocate resources, maintain system stability, and manage energy. The purpose of this study is to use the Quantile Trans
Externí odkaz:
https://doaj.org/article/b592faa46bef4b90b3d23ad57c74f673
Autor:
LuPing Dai
Publikováno v:
Heliyon, Vol 10, Iss 16, Pp e35273- (2024)
With the widespread application of deep learning technology in various fields, power load forecasting, as an important link in power system operation and planning, has also ushered in new opportunities and challenges. Traditional forecasting methods
Externí odkaz:
https://doaj.org/article/25d910a5bacf40ca8ba30ff0dd7de7bd
Publikováno v:
International Journal of Energy Economics and Policy, Vol 14, Iss 4 (2024)
This research develops a new electric charge prediction method by using Convolutional Neural Networks with Quantile Regression (CNN-QR) combined with the Rainbow Technique for Categorical Features (RTCF) and using Deep Learning to create layers for t
Externí odkaz:
https://doaj.org/article/b9bb610c2129408782493b2920db2557
Autor:
Lukas Baur, Konstantin Ditschuneit, Maximilian Schambach, Can Kaymakci, Thomas Wollmann, Alexander Sauer
Publikováno v:
Energy and AI, Vol 16, Iss , Pp 100358- (2024)
Electric Load Forecasting (ELF) is the central instrument for planning and controlling demand response programs, electricity trading, and consumption optimization. Due to the increasing automation of these processes, meaningful and transparent foreca
Externí odkaz:
https://doaj.org/article/cb5c4162c13b49df84e28e0a09b5dc25
Publikováno v:
Heliyon, Vol 10, Iss 7, Pp e28381- (2024)
This paper proposes a new method for short-term electric load forecasting using a Ridgelet Neural Network (RNN) combined with a wavelet transform and optimized by a Self-Adapted (SA) Kho-Kho algorithm (SAKhoKho). The aim of this method is to improve
Externí odkaz:
https://doaj.org/article/7a709374d02e4af3ad539128fe729671
Publikováno v:
Energies, Vol 17, Iss 19, p 4914 (2024)
To ensure the constant availability of electrical energy, power companies must consistently maintain a balance between supply and demand. However, electrical load is influenced by a variety of factors, necessitating the development of robust forecast
Externí odkaz:
https://doaj.org/article/88cd6d52fefe40ae95fe7ae320b6a32e
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
Accurate short-term power load forecasting is essential to balance energy supply and demand, thus minimizing operating costs. However, power load data possesses temporal and nonlinear characteristics, and to mitigate the effects of these factors on t
Externí odkaz:
https://doaj.org/article/3cd9f17a1c5c4ae2af2c0ce11355d0e7
Publikováno v:
Heliyon, Vol 10, Iss 2, Pp e24183- (2024)
Electric load forecasting is a vital task for energy management and policy-making. However, it is also a challenging problem due to the complex and dynamic nature of electric load data. In this paper, a novel technique, called LSV/MOPA, has been prop
Externí odkaz:
https://doaj.org/article/5432bf66c2c34437a83d8b0a1fb1c666
Publikováno v:
Energies, Vol 17, Iss 8, p 1815 (2024)
In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid stability. Short-term load curves are characteristic
Externí odkaz:
https://doaj.org/article/a30a0ebbd00043589668419f3cf14161