COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications
Power prediction is now a crucial part of contemporary energy management systems, which
is important for the organization and administration of renewable resources. Solar and wind …
is important for the organization and administration of renewable resources. Solar and wind …
A comparative study of the data-driven day-ahead hourly provincial load forecasting methods: From classical data mining to deep learning
This paper aims at studying the data-driven short-term provincial load forecasting (STLF)
problem via an in-depth exploration of benefits brought by the feature engineering and …
problem via an in-depth exploration of benefits brought by the feature engineering and …
Hybrid forecasting models for wind-PV systems in diverse geographical locations: performance and power potential analysis
In order to combat the global warming, much stress is put on integrating non-conventional
energy resources, such as wind power plants and solar energy systems, into standard …
energy resources, such as wind power plants and solar energy systems, into standard …
TCAMS-Trans: Efficient temporal-channel attention multi-scale transformer for net load forecasting
Q Zhang, S Zhou, B Xu, X Li - Computers and Electrical Engineering, 2024 - Elsevier
Accurate net load forecasting contributes to increasing the integration of renewable energy
sources and reducing the operating cost of the power grid. In recent years, deep learning …
sources and reducing the operating cost of the power grid. In recent years, deep learning …
Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting
Integration of energy systems with information technologies has facilitated the realization of
smart energy systems that utilize information to optimize system operation. To that end …
smart energy systems that utilize information to optimize system operation. To that end …
Dynamical investigation, electronic circuit realization and emulation of a fractional-order chaotic three-echelon supply chain system
Q Ding, OA Abba, H Jahanshahi, MO Alassafi… - Mathematics, 2022 - mdpi.com
This study is concerned with dynamical investigation, electrical circuit realization, and
emulation of a fractional three-echelon supply chain system. In the financial realm, long-term …
emulation of a fractional three-echelon supply chain system. In the financial realm, long-term …
Implementing very-short-term forecasting of residential load demand using a deep neural network architecture
The need for and interest in very-short-term load forecasting (VSTLF) is increasing and
important for goals such as energy pricing markets. There is greater challenge in predicting …
important for goals such as energy pricing markets. There is greater challenge in predicting …
Gaussian process regression method for forecasting of mortality rates
R Wu, B Wang - Neurocomputing, 2018 - Elsevier
Gaussian process regression (GPR) has long been shown to be a powerful and effective
Bayesian nonparametric approach, and has been applied to a wide range of fields. In this …
Bayesian nonparametric approach, and has been applied to a wide range of fields. In this …
Synergism of deep neural network and elm for smart very-short-term load forecasting
M Alamaniotis - 2019 IEEE PES Innovative Smart Grid …, 2019 - ieeexplore.ieee.org
Load forecasting has been identified as one of the cornerstones in efficiently managing the
power grid. However, accurate forecasting is high challenging due to the inherent …
power grid. However, accurate forecasting is high challenging due to the inherent …
Monthly load forecasting using kernel based gaussian process regression
Forecasting of electricity load for a month is crucial for power system planning and safe
operation. Monthly demand is subject to various factors such as season and climate effects …
operation. Monthly demand is subject to various factors such as season and climate effects …