An effective hybrid NARX-LSTM model for point and interval PV power forecasting
This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique
based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with …
based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with …
PLS-CNN-BiLSTM: An end-to-end algorithm-based Savitzky–Golay smoothing and evolution strategy for load forecasting
This paper proposes an effective deep learning framework for Short-Term Load Forecasting
(STLF) of multivariate time series. The proposed model consists of a hybrid Convolutional …
(STLF) of multivariate time series. The proposed model consists of a hybrid Convolutional …
Attention based long-term air temperature forecasting network: ALTF Net
Air temperature is one of the most important meteorological parameters related with
atmospheric and environmental research. In this context, accurate prediction and forecasting …
atmospheric and environmental research. In this context, accurate prediction and forecasting …
A hybrid Bayesian ridge regression-CWT-catboost model for PV power forecasting
The forecasting of the high intermittency of Photovoltaic (PV) energy in smart grid is a
persisting challenge. The proposed paper takes this challenge by presenting accurate …
persisting challenge. The proposed paper takes this challenge by presenting accurate …
Enhanced random forest model for robust short-term photovoltaic power forecasting using weather measurements
Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost
importance in smart grids. The deployment of STPF techniques provides fast dispatching in …
importance in smart grids. The deployment of STPF techniques provides fast dispatching in …
On the pivotal role of artificial intelligence toward the evolution of smart grids: A review of advanced methodologies and applications
This chapter addresses the status of artificial intelligence (AI) as a central element in smart
grid (SG) while focusing on the recent progress of research on machine learning techniques …
grid (SG) while focusing on the recent progress of research on machine learning techniques …
Short-term electric load forecasting based on data-driven deep learning techniques
Accurate Short-Term Load Forecasting (STLF) has been considered a topic of extreme
importance for efficient energy management, reliable energy transactions, and economic …
importance for efficient energy management, reliable energy transactions, and economic …
Forecasting chaotic weather variables with echo state networks and a novel swing training approach
Physical systems like weather variables, the behavior of oceanic bodies, economic
variables, and similar systems possess a considerable amount of chaos, and deducing …
variables, and similar systems possess a considerable amount of chaos, and deducing …
Fog intelligence for energy efficient management in smart street lamps
Street lamp is a great asset for human society with a narrow beam spread light. The
extensive proliferation of solar power in street lamps causes power outages due to their …
extensive proliferation of solar power in street lamps causes power outages due to their …
Investigating the Influence of Temperature on UAV Signal Quality.
Advancements in drone technology make them important in many areas. military, industry,
and disaster The efficacy of a drone's communication systems can be greatly impacted by …
and disaster The efficacy of a drone's communication systems can be greatly impacted by …