Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges
The integration of large-scale wind power introduces issues in modern power systems
operations due to its strong randomness and volatility. These issues can be resolved via …
operations due to its strong randomness and volatility. These issues can be resolved via …
A review of machine learning algorithms used for load forecasting at microgrid level
E Mele - Sinteza 2019-International Scientific …, 2019 - portal.sinteza.singidunum.ac.rs
As load forecasting nowadays is a crucial and integral part of the energy production
procedures a large number of forecasting methods has been pro-posed to address it …
procedures a large number of forecasting methods has been pro-posed to address it …
Survey of machine learning and deep learning approaches on sales forecasting
MPA Dharshini, SA Vijila - 2021 4th International Conference …, 2021 - ieeexplore.ieee.org
Sales forecasting plays an important role in the modern financial system. It is used in the
private and government financial institutions, companies, industries, factories, trading, etc …
private and government financial institutions, companies, industries, factories, trading, etc …
[PDF][PDF] Industrial energy load profile forecasting under enhanced time of use tariff (ETOU) using artificial neural network
MF Sulaima, SAA Hanipah… - International …, 2020 - pdfs.semanticscholar.org
The demand response program involves consumers to mitigate peak demand and reducing
global CO2 emission. In sustaining this effort, energy provider such as Tenaga Nasional …
global CO2 emission. In sustaining this effort, energy provider such as Tenaga Nasional …
Electricity use profiling and forecasting at microgrid level
Short-Term Load Forecasting (STLF) is nowadays a crucial and integral part of the energy
production procedure to the emerging technologies for demand side management. The …
production procedure to the emerging technologies for demand side management. The …
[PDF][PDF] Machine learning platform for profiling and forecasting at microgrid level
The shift towards distributed generation and microgrids has renewed the interest in
forecasting algorithms and methods, which need to take into account the advances in …
forecasting algorithms and methods, which need to take into account the advances in …
Technical feasible study for future solar thermal steam power station in Malaysia
This paper proposed renewable energy which is potential to be used in Malaysia in
generating electricity to innovate and improve current operating systems. Thermal and water …
generating electricity to innovate and improve current operating systems. Thermal and water …
DETERMINATION OF THE OPTIMUM LOAD PROFILE UNDER ENHANCED OF USE TARIFF (ETOU) SCHEME USING COMBINATION OF OPTIMIZATION …
Demand side management (DSM) has been conventionally adopted in many ways to
efficiently managing the appropriate electricity loads. However, with the sophisticated design …
efficiently managing the appropriate electricity loads. However, with the sophisticated design …
A Hybrid Method of Self Organizing Maps with Statistical Feature Extraction for Accurate and Efficient Partial Discharge Recognition and Clustering
Partial discharge is the phenomena that affecting the health of power transformer. The
problem with delay in identifying will deteriorate the transformer insulation condition and …
problem with delay in identifying will deteriorate the transformer insulation condition and …
Transformer mechanical integrity evaluation via unsupervised neural network (UNN) in smart grid network
This paper describes the classification of mechanical integrity of transformers using
unsupervised neural networks (UNN). Transformers are the integral part of electrical system …
unsupervised neural networks (UNN). Transformers are the integral part of electrical system …