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Short-term electric power load forecasting using random forest and gated recurrent unit
The main purpose of this paper is to develop an efficient machine learning model to estimate
the electric power load. The developed machine learning model can be used by electric …
the electric power load. The developed machine learning model can be used by electric …
Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting
Load forecasting implies directly in financial return and information for electrical systems
planning. A framework to build wavenet ensemble for short-term load forecasting is …
planning. A framework to build wavenet ensemble for short-term load forecasting is …
Multi-objective prediction intervals for wind power forecast based on deep neural networks
M Zhou, B Wang, S Guo, J Watada - Information Sciences, 2021 - Elsevier
Wind power forecast is playing a significant role in the operation and dispatch of modern
power systems. Compared with traditional point forecast methods, interval forecast is able to …
power systems. Compared with traditional point forecast methods, interval forecast is able to …
Remaining energy estimation for lithium-ion batteries via Gaussian mixture and Markov models for future load prediction
Other than upgrading the energy storage technology employed within electric vehicles
(EVs), improving the driving range estimation methods will help to reduce the phenomena …
(EVs), improving the driving range estimation methods will help to reduce the phenomena …
[HTML][HTML] Designing a short-term load forecasting model in the urban smart grid system
C Li - Applied Energy, 2020 - Elsevier
The transition of the energy system from fossil fuel towards renewable energy (RE) is rising
sharply, which provides a cleaner energy source to the urban smart grid system. However …
sharply, which provides a cleaner energy source to the urban smart grid system. However …
State of power prediction for lithium-ion batteries in electric vehicles via wavelet-Markov load analysis
Electric vehicle (EV) power demands come from its acceleration/braking as well as
consumptions of the components. The power delivered to meet any demand is limited to the …
consumptions of the components. The power delivered to meet any demand is limited to the …
Feature-fusion-kernel-based Gaussian process model for probabilistic long-term load forecasting
In this paper, we present a feature fusion method designed for the Gaussian process
model's kernel functions for the probabilistic long-term load forecasting. To enrich the …
model's kernel functions for the probabilistic long-term load forecasting. To enrich the …
Probabilistic electric load forecasting through Bayesian mixture density networks
This work presents a novel approach to address a challenging and still unsolved problem of
neural network based load forecasting systems, that despite the significant results reached …
neural network based load forecasting systems, that despite the significant results reached …
A hybrid approach for energy consumption forecasting with a new feature engineering and optimization framework in smart grid
Electric energy consumption forecasting enables distribution system operators to perform
efficient energy management by flexibly engaging energy consumers under the intelligent …
efficient energy management by flexibly engaging energy consumers under the intelligent …
Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities
Electric load estimation is an important activity for electrical power system operators to
operate the system stably and optimally. This paper develops a machine learning model …
operate the system stably and optimally. This paper develops a machine learning model …