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 …
Leveraging hypernetworks and learnable kernels for consumer energy forecasting across diverse consumer types
MU Danish, K Grolinger - IEEE Transactions on Power Delivery, 2024 - ieeexplore.ieee.org
Consumer energy forecasting is essential for managing energy consumption and planning,
directly influencing operational efficiency, cost reduction, personalized energy management …
directly influencing operational efficiency, cost reduction, personalized energy management …
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 …
An adaptive evolutionary neural network model for load management in smart grid environment
To empower the management of smart meters' demand load within a smart grid
environment, this paper presents a Feed-forward Neural Network with ADaptive …
environment, this paper presents a Feed-forward Neural Network with ADaptive …
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 …
Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Survey
Short-Term Electricity-Load Forecasting (STELF) refers to the prediction of the immediate
demand (in the next few hours to several days) for the power system. Various external …
demand (in the next few hours to several days) for the power system. Various external …
Hybrid Data-Driven Parameters Estimation for Communication-less WPT System with Reduced Primary Sampling Data
B Cheng, L He, H Liu - IEEE Transactions on Transportation …, 2024 - ieeexplore.ieee.org
Wireless power transfer (WPT) systems advantaging environment-friendly and highly
efficient, and accurate parameter estimation is a premise for precise control and a guarantee …
efficient, and accurate parameter estimation is a premise for precise control and a guarantee …