A comprehensive review of the load forecasting techniques using single and hybrid predictive models
A Al Mamun, M Sohel, N Mohammad… - IEEE …, 2020 - ieeexplore.ieee.org
Load forecasting is a pivotal part of the power utility companies. To provide load-shedding
free and uninterrupted power to the consumer, decision-makers in the utility sector must …
free and uninterrupted power to the consumer, decision-makers in the utility sector must …
Review of Short‐Term Load Forecasting for Smart Grids Using Deep Neural Networks and Metaheuristic Methods
Forecasting electricity load demand is critical for power system planning and energy
management. In particular, accurate short‐term load forecasting (STLF), which focuses on …
management. In particular, accurate short‐term load forecasting (STLF), which focuses on …
[HTML][HTML] Solving the cold-start problem in short-term load forecasting using tree-based methods
An energy-management system requires accurate prediction of the electric load for optimal
energy management. However, if the amount of electric load data is insufficient, it is …
energy management. However, if the amount of electric load data is insufficient, it is …
[HTML][HTML] Bayesian optimized echo state network applied to short-term load forecasting
G Trierweiler Ribeiro, J Guilherme Sauer… - Energies, 2020 - mdpi.com
Load forecasting impacts directly financial returns and information in electrical systems
planning. A promising approach to load forecasting is the Echo State Network (ESN), a …
planning. A promising approach to load forecasting is the Echo State Network (ESN), a …
[HTML][HTML] A data-driven model to forecast multi-step ahead time series of Turkish daily electricity load
KD Ünlü - Electronics, 2022 - mdpi.com
It is critical to maintain a balance between the supply and the demand for electricity because
of its non-storable feature. For power-producing facilities and traders, an electrical load is a …
of its non-storable feature. For power-producing facilities and traders, an electrical load is a …
Self-updating machine learning system for building load forecasting-method, implementation and case-study on COVID-19 impact
Y Besanger, QT Tran - Sustainable Energy, Grids and Networks, 2022 - Elsevier
Accurate building load forecasting is a challenging task due to the large volume of input
information, their non-linearity and variant nature due to human activities. In this study, we …
information, their non-linearity and variant nature due to human activities. In this study, we …
Short-Term Load Forecasting: A Comprehensive Review and Simulation Study With CNN-LSTM Hybrids Approach
Short-term load forecasting (STLF) is vital in effectively managing the reserve requirement in
modern power grids. Subsequently, it supports the grid operator in making effective and …
modern power grids. Subsequently, it supports the grid operator in making effective and …
Short term Markov corrector for building load forecasting system–Concept and case study of day-ahead load forecasting under the impact of the COVID-19 pandemic
Y Besanger - Energy and Buildings, 2022 - Elsevier
In this paper, we present the concept and formulation of a short-term Markov corrector to an
underlying day-ahead building load forecasting model. The models and the correctors are …
underlying day-ahead building load forecasting model. The models and the correctors are …
A brief review of condition monitoring techniques for the induction motor
D Kumar, J Daudpoto - … of the Canadian Society for Mechanical …, 2019 - cdnsciencepub.com
The induction motor is widely used in industry owing to its simple construction and low cost.
In this paper, we present a state-of-the-art review of condition monitoring techniques for the …
In this paper, we present a state-of-the-art review of condition monitoring techniques for the …
[HTML][HTML] Hyperparameter Tuning of Load-Forecasting Models Using Metaheuristic Optimization Algorithms—A Systematic Review
Load forecasting is an integral part of the power industries. Load-forecasting techniques
should minimize the percentage error while prediction future demand. This will inherently …
should minimize the percentage error while prediction future demand. This will inherently …