A comprehensive review of the load forecasting techniques using single and hybrid predictive models
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 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 …
Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid
Accurate electric load forecasting is important due to its application in the decision making
and operation of the power grid. However, the electric load profile is a complex signal due to …
and operation of the power grid. However, the electric load profile is a complex signal due to …
Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador
G López, P Arboleya - Renewable Energy, 2022 - Elsevier
Wind speed forecasting systems over complex terrain at high altitude are very complex and
conventional forecasting systems are unable to be applied due to wind variability. This study …
conventional forecasting systems are unable to be applied due to wind variability. This study …
Detection of false data injection cyber-attacks in DC microgrids based on recurrent neural networks
Cyber-physical systems (CPSs) are vulnerable to cyber-attacks. Nowadays, the detection of
cyber-attacks in microgrids as examples of CPS has become an important topic due to their …
cyber-attacks in microgrids as examples of CPS has become an important topic due to their …
An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting
Due to high penetration of distributed energy resources, integration of intermittent renewable
energy resources and deployment of demand-side management, highly accurate short-term …
energy resources and deployment of demand-side management, highly accurate short-term …
A review of applications of artificial intelligent algorithms in wind farms
Wind farms are enormous and complex control systems. It is challenging and valuable to
control and optimize wind farms. Their applications are widely used in various industries …
control and optimize wind farms. Their applications are widely used in various industries …
A novel short receptive field based dilated causal convolutional network integrated with Bidirectional LSTM for short-term load forecasting
Abstract The Short-Term Load Forecasting (STLF) is a pre-eminent task for reliable power
generation and electrical load dispatching in the power system. Numerous machine …
generation and electrical load dispatching in the power system. Numerous machine …
Modeling energy demand—a systematic literature review
PA Verwiebe, S Seim, S Burges, L Schulz… - Energies, 2021 - mdpi.com
In this article, a systematic literature review of 419 articles on energy demand modeling,
published between 2015 and 2020, is presented. This provides researchers with an …
published between 2015 and 2020, is presented. This provides researchers with an …
Improved deep belief network for short-term load forecasting considering demand-side management
X Kong, C Li, F Zheng, C Wang - IEEE transactions on power …, 2019 - ieeexplore.ieee.org
Demand-side management (DSM) increases the complexity of forecasting environment,
which makes traditional forecasting methods difficult to meet the firm's need for predictive …
which makes traditional forecasting methods difficult to meet the firm's need for predictive …