Consumers profiling based federated learning approach for energy load forecasting
Energy load estimation is critical for the smooth functioning of several activities, such as
reliable supply, reduced wastage, decision making and generation planning tasks. So far …
reliable supply, reduced wastage, decision making and generation planning tasks. So far …
An lstm-sae-based behind-the-meter load forecasting method
Nowadays, modern technologies in power systems have been attracting more attention, and
households can supply a portion of or all of their electricity based on on-site generation at …
households can supply a portion of or all of their electricity based on on-site generation at …
Post-earthquake rapid assessment for loop system in substation using ground motion signals
W Zhu, Q Xie - Mechanical Systems and Signal Processing, 2024 - Elsevier
This study proposes a rapid assessment framework for loop systems in substations after
earthquakes, in which multiple one-to-one machine learning (ML) models are established …
earthquakes, in which multiple one-to-one machine learning (ML) models are established …
A power load forecasting method based on intelligent data analysis
H Liu, X Xiong, B Yang, Z Cheng, K Shao, A Tolba - Electronics, 2023 - mdpi.com
Abnormal electricity consumption behavior not only affects the safety of power supply but
also damages the infrastructure of the power system, posing a threat to the secure and …
also damages the infrastructure of the power system, posing a threat to the secure and …
From time-series to hybrid models: advancements in short-term load forecasting embracing smart grid paradigm
This review paper is a foundational resource for power distribution and management
decisions, thoroughly examining short-term load forecasting (STLF) models within power …
decisions, thoroughly examining short-term load forecasting (STLF) models within power …
Multi-term electrical load forecasting of smart cities using a new hybrid highly accurate neural network-based predictive model
This paper presents FARHAN, a novel hybrid model designed to address the challenges of
electrical load forecasting in smart grids. FARHAN combines descending neuron attention …
electrical load forecasting in smart grids. FARHAN combines descending neuron attention …
Residential short term load forecasting based on federated learning
Load forecasting is an essential task in the power industry as an important means to assist
the grid to balance supply demand. A large amount of user data monitored by smart grids …
the grid to balance supply demand. A large amount of user data monitored by smart grids …
Modeling temporal dual variations for return air temperature prediction of mK-level temperature-controlled clean chamber
H Yu, H Dong, Z Zeng, D Cao, W Zhang… - Journal of Building …, 2024 - Elsevier
Clean chamber with mK-level temperature stability is required for precision manufacturing
and optical equipment to achieve nanoscale manufacturing tolerances and measurement …
and optical equipment to achieve nanoscale manufacturing tolerances and measurement …
A Unifying Framework for Short-Term Load Forecasting via EM-CCGPBOATT Methodology
J Xing, J Su, Y Xue, X Chang, Z Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Short-term load forecasting (STLF) has long been a crucial research topic in energy
management since it is significant to the secure and reliable operation of energy systems …
management since it is significant to the secure and reliable operation of energy systems …
Enhancing HVAC Electricity Load Prediction Accuracy using Bi-LSTM Method based on Daily Dataset
This study focuses on HVAC electricity load prediction in a smart building using two neural
network models, LSTM and Bi-LSTM. We created models based on daily dataset separately …
network models, LSTM and Bi-LSTM. We created models based on daily dataset separately …