Results 51 to 60 of about 8,580 (164)

Industrial Ultra-Short-Term Load Forecasting With Data Completion

IEEE Access, 2020
Accurate and efficient ultra-short-term load forecasting is crucial for industrial power users to have stabilized and optimized operations. In this paper, we develop novel strategies for industrial power users to handle their challenges in ultra-short ...
Haoyu Jiang, Angjian Wu, Bo Wang, Peizhe Xu, Gang Yao   +4 more
doaj   +1 more source

Short-term power load forecasting in distribution networks considering human comfort level

Frontiers in Energy Research
The growth of power demand and the increase of new energy penetration have resulted in a heightened necessity for the precision of short-term power load forecasting in distribution networks. The majority of current research on short-term load forecasting
Yong Li, Hua Wang, Xianbin Huang, Juanyang Hao, Wenting Lei, Quanlin Wang   +5 more
doaj   +1 more source

Research on Short-term Load Forecasting Algorithm Based on VMD and TCN

Journal of Harbin University of Science and Technology
Aiming at the low accuracy of short-term load forecasting in substation area, a temporal convolutional network short-term load forecasting algorithm based on variational mode decomposition is proposed in this paper.
WANG Qing, CHEN Zhiru, LI Guimin, JING Zhen, ZHANG Zhi, WANG Pingxin, CUI Qi   +6 more
doaj   +1 more source

Short-Term Load Forecasting Based on RS-ART [PDF]

, 2011
This paper presents a short-term electric load forecasting method based on Autoregressive Tree Algorithm and Rough Set Theory. Firstly, Rough Set Theory was used to reduce the testing properties of Autoregressive Tree. It can optimize the Autoregressive Tree Algorithm.
Yang, Tao, Zhang, Feng, Li, Qingji, Yang, Ping   +3 more
openaire   +2 more sources

Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Fruit Fly Optimization Algorithm

Journal of Electrical and Computer Engineering, 2015
Electric power is a kind of unstorable energy concerning the national welfare and the people’s livelihood, the stability of which is attracting more and more attention.
Wei Sun, Minquan Ye
doaj   +1 more source

Stacking for Probabilistic Short-Term Load Forecasting

In this study, we delve into the realm of meta-learning to combine point base forecasts for probabilistic short-term electricity demand forecasting. Our approach encompasses the utilization of quantile linear regression, quantile regression forest, and post-processing techniques involving residual simulation to generate quantile forecasts. Furthermore,
openaire   +2 more sources

A hybrid model of modal decomposition and gated recurrent units for short-term load forecasting. [PDF]

PeerJ Comput Sci, 2023
Wang CH, Li WQ.
europepmc   +1 more source

Short-Term Load Forecasting 2019 [PDF]

, 2021
Antonio Gabaldón, María Carmen Ruiz-Abellón, Luis Alfredo Fernández-Jiménez   +2 more
openaire   +3 more sources

Research on Load Forecasting Based on Bayesian Optimized CNN-LSTM Neural Network

Energies
With the high penetration of renewable energy integration and massive user participation in electricity markets, traditional short-term load forecasting methods exhibit limitations in both adaptability and prediction accuracy.
Pengyang Duan, Huannian Jiao, Jianying Sun, Aiming Han, Zheng Dai, Liang Cheng, Xiaotao Chen   +6 more
doaj   +1 more source

Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution. [PDF]

PeerJ Comput Sci, 2023
Ullah I, Muhammad Hasanat S, Aurangzeb K, Alhussein M, Rizwan M, Anwar MS.   +5 more
europepmc   +1 more source