Results 61 to 70 of about 108,774 (242)

Short Term Power Load Forecasting Based on PSVMD-CGA Model

Sustainability, 2023
Short-term power load forecasting is critical for ensuring power system stability. A new algorithm that combines CNN, GRU, and an attention mechanism with the Sparrow algorithm to optimize variational mode decomposition (PSVMD–CGA) is proposed to address the problem of the effect of random load fluctuations on the accuracy of short-term load ...
Jingming Su, Xuguang Han, Yan Hong
openaire   +1 more source

Short-term load estimation based on improved DBN-LSTM

Sustainable Energy Research
Aiming at the rapid change and low forecasting accuracy of short-term power load forecasting, a forecasting model based on the improved deep belief network and long short-term memory network is proposed.
Nan Dong, Yuwen Wu, Buyun Su, Zhanzhi Liu   +3 more
doaj   +1 more source

Short-Term Load Forecasting Using a Novel Deep Learning Framework

Energies, 2018
Short-term load forecasting is the basis of power system operation and analysis. In recent years, the use of a deep belief network (DBN) for short-term load forecasting has become increasingly popular. In this study, a novel deep-learning framework based
Xiaoyu Zhang, Rui Wang, Tao Zhang, Yajie Liu, Yabing Zha   +4 more
doaj   +1 more source

Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting

, 2019
The smart metering infrastructure has changed how electricity is measured in both residential and industrial application. The large amount of data collected by smart meter per day provides a huge potential for analytics to support the operation of a ...
D Alahakoon, Damminda Alahakoon, H-T Pao, J Barton, K Kwang-Ho, K Zhao, L Han, MQ Raza, MR Fernández, P Mirowski, P-H Kuo, S Hochreiter, SV Verdu, W Kong, Y Wang   +14 more
core   +1 more source

A short-term electricity load forecasting method integrating empirical modal decomposition with SAM-LSTM

Frontiers in Energy Research
Short-term power load forecasting is the basis for ensuring the safe and stable operation of the power system. However, because power load forecasting is affected by weather, economy, geography, and other factors, it has strong instability and ...
Yafangzi Zhou, Zhiyin Su, Kun Gao, Zhengwen Wang, Wei Ye, Jinhui Zeng   +5 more
doaj   +1 more source

Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree [PDF]

, 2022
Venkataramana Veeramsetty, Modem Sai Pavan Kumar, Surender Reddy Salkuti   +2 more
openalex   +1 more source

Short-term power load forecasting based on ARIMA-LSTM

Journal of Physics: Conference Series
Abstract Accurate short-term forecasting of power load demand has become increasingly crucial in the field of electric systems due to the continuous development of society. This paper utilizes the ARIMA and the LSTM algorithm to forecast the future 24-hour electricity load of a region.
Ruiyan Zhou, Xingchen Zhang
openaire   +1 more source

A hybrid prediction method for short‐term load based on temporal convolutional networks and attentional mechanisms

IET Generation, Transmission & Distribution
Accurate power load prediction is an important guide for power system planning and operation. High‐ or low‐load prediction results will affect the operation of the power system.
Min Li, Hangwei Tian, Qinghui Chen, Mingle Zhou, Gang Li   +4 more
doaj   +1 more source

Short-Term Load Forecasting using Artificial Neural Network Techniques [PDF]

, 2009
Artificial Neural Network (ANN) Method is applied to fore cast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day pattern includes Saturdays, Sunday and Monday loads.
Kumar, Manoj
core  

Short-Term Load Forecasting Using The Time Series And Artificial Neural Network Methods [PDF]

, 2016
Forecasting of electrical load is very crucial to the effective and efficient operation of any power system. This is achieved by obtaining the most accurate forecast which help in minimizing the risk in decision making and reducesthe costs of operating
Amaize, Peter, Awelewa, A. A., Ojewola, Tolulope, Samuel, I. A.   +3 more
core