Load forecasting models in smart grid using smart meter information: a review

F Dewangan, AY Abdelaziz, M Biswal - Energies, 2023 - mdpi.com
The smart grid concept is introduced to accelerate the operational efficiency and enhance
the reliability and sustainability of power supply by operating in self-control mode to find and …

[PDF][PDF] Methods and models for electric load forecasting: a comprehensive review

MA Hammad, B Jereb, B Rosi… - Logist. Sustain …, 2020 - intapi.sciendo.com
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 …

Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning …

U Şahin, S Ballı, Y Chen - Applied Energy, 2021 - Elsevier
Balances in the energy sector have changed since the implementation of the Covid-19
pandemic lockdown in Europe. This paper analyses how the lockdown affected electricity …

Individualized short-term electric load forecasting using data-driven meta-heuristic method based on LSTM network

L Sun, H Qin, K Przystupa, M Majka, O Kochan - Sensors, 2022 - mdpi.com
Short-term load forecasting is viewed as one promising technology for demand prediction
under the most critical inputs for the promising arrangement of power plant units. Thus, it is …

A deep LSTM‐CNN based on self‐attention mechanism with input data reduction for short‐term load forecasting

S Yi, H Liu, T Chen, J Zhang… - … Transmission & Distribution, 2023 - Wiley Online Library
Numerous studies on short‐term load forecasting (STLF) have used feature extraction
methods to increase the model's accuracy by incorporating multidimensional features …

[HTML][HTML] Electrical demand aggregation effects on the performance of deep learning-based short-term load forecasting of a residential building

A Shaqour, T Ono, A Hagishima, H Farzaneh - Energy and AI, 2022 - Elsevier
Modern power grids face the challenge of increasing renewable energy penetration that is
stochastic in nature and calls for accurate demand predictions to provide the optimized …

Accurate ultra-short-term load forecasting based on load characteristic decomposition and convolutional neural network with bidirectional long short-term memory …

M Zhang, Y Han, AS Zalhaf, C Wang, P Yang… - … Energy, Grids and …, 2023 - Elsevier
Aiming at the continuous, periodic, and nonlinear characteristics of load changes, this paper
proposes a combined ultra-short-term load forecasting model based on improved complete …

Design and development of Residential Sector Load Prediction model during COVID-19 Pandemic using LSTM based RNN

A Ajitha, M Goel, M Assudani, S Radhika… - Electric Power Systems …, 2022 - Elsevier
Covid-19 pandemic and resulting lockdown has created a wide impact on social life,
including sudden rise in residential load demand. Utilities, for better load scheduling and …

A hybrid approach for measuring the vibrational trend of hydroelectric unit with enhanced multi-scale chaotic series analysis and optimized least squares support …

W Fu, K Wang, C Zhang, J Tan - Transactions of the Institute …, 2019 - journals.sagepub.com
Accurate vibrational trend measuring for hydroelectric unit (HEU) is of great significance for
safe and economic operation of unit. For this purpose, a novel hybrid approach based on …

A review on short‐term load forecasting models for micro‐grid application

VY Kondaiah, B Saravanan… - The Journal of …, 2022 - Wiley Online Library
Load forecasting (LF), particularly short‐term load forecasting (STLF), plays a vital role
throughout the operation of the conventional power system. The precise modelling and …