Convolutional and recurrent neural network based model for short-term load forecasting

H Eskandari, M Imani, MP Moghaddam - Electric Power Systems Research, 2021 - Elsevier
The consumed electrical load is affected by many external factors such as weather, season
of the year, weekday or weekend and holiday. In this paper, it is tried to provide a high …

Electric power load forecasting based on multivariate LSTM neural network using Bayesian optimization

M Munem, TMR Bashar, MH Roni… - 2020 IEEE Electric …, 2020 - ieeexplore.ieee.org
With rapid growth and development around the world, electricity consumption is increasing
day by day. As the production and consumption of electricity is simultaneous, an electric …

Short-term electric load forecasting based on data-driven deep learning techniques

M Massaoudi, SS Refaat, I Chihi… - IECON 2020 The …, 2020 - ieeexplore.ieee.org
Accurate Short-Term Load Forecasting (STLF) has been considered a topic of extreme
importance for efficient energy management, reliable energy transactions, and economic …

Deep neural network and long short-term memory for electric power load forecasting

N Son, S Yang, J Na - Applied Sciences, 2020 - mdpi.com
Forecasting domestic and foreign power demand is crucial for planning the operation and
expansion of facilities. Power demand patterns are very complex owing to energy market …

[HTML][HTML] General short-term load forecasting based on multi-task temporal convolutional network in COVID-19

Z Zhang, J Liu, S Pang, M Shi, HH Goh, Y Zhang… - International Journal of …, 2023 - Elsevier
The spread of the global COVID-19 epidemic has resulted in significant shifts in electricity
consumption compared to regular days. It is unknown if standard single-task, single …

Advancements in Household Load Forecasting: Deep Learning Model with Hyperparameter Optimization

HA Al-Jamimi, GM BinMakhashen, MY Worku… - Electronics, 2023 - mdpi.com
Accurate load forecasting is of utmost importance for modern power generation facilities to
effectively meet the ever-changing electricity demand. Predicting electricity consumption is a …

A novel evolutionary-based deep convolutional neural network model for intelligent load forecasting

SMJ Jalali, S Ahmadian, A Khosravi… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
The problem of electricity load forecasting has emerged as an essential topic for power
systems and electricity markets seeking to minimize costs. However, this topic has a high …

[HTML][HTML] An improved LSTM-Seq2Seq-based forecasting method for electricity load

Y Mu, M Wang, X Zheng, H Gao - Frontiers in Energy Research, 2023 - frontiersin.org
Power load forecasting has gained considerable research interest in recent years. The
power load is vulnerable to randomness and uncertainty during power grid operations …

Short-term load forecasting for industrial customers based on TCN-LightGBM

Y Wang, J Chen, X Chen, X Zeng… - … on Power Systems, 2020 - ieeexplore.ieee.org
Accurate and rapid load forecasting for industrial customers has been playing a crucial role
in modern power systems. Due to the variability of industrial customers' activities, individual …

Short-term load forecasting based on SARIMAX-LSTM

F Sheng, L Jia - 2020 5th International Conference on Power …, 2020 - ieeexplore.ieee.org
Load forecasting has been the focus of energy management system. In recent years, in
addition to some traditional time series forecasting models, with the continuous development …