Electricity load forecasting: a systematic review
IK Nti, M Teimeh, O Nyarko-Boateng… - Journal of Electrical …, 2020 - Springer
The economic growth of every nation is highly related to its electricity infrastructure, network,
and availability since electricity has become the central part of everyday life in this modern …
and availability since electricity has become the central part of everyday life in this modern …
[PDF][PDF] Methods and models for electric load forecasting: a comprehensive review
Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and
plays a crucial role in electric capacity scheduling and power systems management and …
plays a crucial role in electric capacity scheduling and power systems management and …
A short-term load forecasting method using integrated CNN and LSTM network
In this study, a new technique is proposed to forecast short-term electrical load. Load
forecasting is an integral part of power system planning and operation. Precise forecasting …
forecasting is an integral part of power system planning and operation. Precise forecasting …
Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting
Accurate day-ahead peak load forecasting is crucial not only for power dispatching but also
has a great interest to investors and energy policy maker as well as government. Literature …
has a great interest to investors and energy policy maker as well as government. Literature …
Day-ahead load demand forecasting in urban community cluster microgrids using machine learning methods
The modern-day urban energy sector possesses the integrated operation of various
microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility …
microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility …
HSIC bottleneck based distributed deep learning model for load forecasting in smart grid with a comprehensive survey
Load forecasting is a vital part of smart grids for predicting the required electrical power
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …
A deep bi-directional long-short term memory neural network-based methodology to enhance short-term electricity load forecasting for residential applications
Unexpected fluctuations associated with electricity load consumption patterns pose a
significant threat to the stability, efficiency, and sustainability of modernized energy systems …
significant threat to the stability, efficiency, and sustainability of modernized energy systems …
A short-term preventive maintenance scheduling method for distribution networks with distributed generators and batteries
Preventive maintenance is applied in distribution networks to prevent failures by performing
maintenance actions on components that are at risk. Distributed generators (DGs) and …
maintenance actions on components that are at risk. Distributed generators (DGs) and …
Data-driven short term load forecasting with deep neural networks: Unlocking insights for sustainable energy management
W Waheed, Q Xu - Electric Power Systems Research, 2024 - Elsevier
In today's smart grid and building infrastructure, it is strongly suggested to implement short-
term demand forecasting for future power generation. There is a growing demand for …
term demand forecasting for future power generation. There is a growing demand for …
Short-term aggregated residential load forecasting using BiLSTM and CNN-BiLSTM
B Bohara, RI Fernandez… - … on Innovation and …, 2022 - ieeexplore.ieee.org
Higher penetration of renewable and smart home technologies at the residential level
challenges grid stability as utility-customer interactions add complexity to power system …
challenges grid stability as utility-customer interactions add complexity to power system …