State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques
Forecasting renewable energy efficiency significantly impacts system management and
operation because more precise forecasts mean reduced risk and improved stability and …
operation because more precise forecasts mean reduced risk and improved stability and …
Load forecasting models in smart grid using smart meter information: a review
F Dewangan, AY Abdelaziz, M Biswal - Energies, 2023 - mdpi.com
The smart grid concept is introduced to accelerate the operational efficiency and enhance
the reliability and sustainability of power supply by operating in self-control mode to find and …
the reliability and sustainability of power supply by operating in self-control mode to find and …
Prediction of energy production level in large pv plants through auto-encoder based neural-network (auto-nn) with restricted boltzmann feature extraction
In general, reliable PV generation prediction is required to increase complete control quality
and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV …
and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV …
A comprehensive review of the load forecasting techniques using single and hybrid predictive models
A Al Mamun, M Sohel, N Mohammad… - IEEE …, 2020 - ieeexplore.ieee.org
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 …
Methods of forecasting electric energy consumption: A literature review
RV Klyuev, ID Morgoev, AD Morgoeva, OA Gavrina… - Energies, 2022 - mdpi.com
Balancing the production and consumption of electricity is an urgent task. Its implementation
largely depends on the means and methods of planning electricity production. Forecasting is …
largely depends on the means and methods of planning electricity production. Forecasting is …
Short-term load forecasting using EMD-LSTM neural networks with a Xgboost algorithm for feature importance evaluation
H Zheng, J Yuan, L Chen - Energies, 2017 - mdpi.com
Accurate load forecasting is an important issue for the reliable and efficient operation of a
power system. This study presents a hybrid algorithm that combines similar days (SD) …
power system. This study presents a hybrid algorithm that combines similar days (SD) …
Electrical load forecasting models: A critical systematic review
C Kuster, Y Rezgui, M Mourshed - Sustainable cities and society, 2017 - Elsevier
Electricity forecasting is an essential component of smart grid, which has attracted
increasing academic interest. Forecasting enables informed and efficient responses for …
increasing academic interest. Forecasting enables informed and efficient responses for …
Enhancing smart grid with microgrids: Challenges and opportunities
The modern electric power systems are going through a revolutionary change because of
increasing demand of electric power worldwide, developing political pressure and public …
increasing demand of electric power worldwide, developing political pressure and public …
Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network
Electric load forecasting plays a vital role in smart grids. Short term electric load forecasting
forecasts the load that is several hours to several weeks ahead. Due to the nonlinear, non …
forecasts the load that is several hours to several weeks ahead. Due to the nonlinear, non …
A short‐term load forecasting method based on GRU‐CNN hybrid neural network model
L Wu, C Kong, X Hao, W Chen - Mathematical problems in …, 2020 - Wiley Online Library
Short‐term load forecasting (STLF) plays a very important role in improving the economy
and stability of the power system operation. With the smart meters and smart sensors widely …
and stability of the power system operation. With the smart meters and smart sensors widely …