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A novel temporal feature selection based LSTM model for electrical short-term load forecasting
An accurate electrical Short-term Load Forecasting (STLF) is an eminent factor in the power
generation, electrical load dispatching and energy planning for the power supply …
generation, electrical load dispatching and energy planning for the power supply …
Enhancing PV hosting capacity and mitigating congestion in distribution networks with deep learning based PV forecasting and battery management
The extensive deployment of domestic photovoltaic (PV) systems may result in exceeding
the limits of the network's PV hosting capacity (HC), which leads to energy delivery …
the limits of the network's PV hosting capacity (HC), which leads to energy delivery …
Neural networks based shunt hybrid active power filter for harmonic elimination
M Iqbal, M Jawad, MH Jaffery, S Akhtar, MN Rafiq… - IEEE …, 2021 - ieeexplore.ieee.org
The growing use of nonlinear devices is introducing harmonics in power system networks
that result in distortion of current and voltage signals causing damage to power distribution …
that result in distortion of current and voltage signals causing damage to power distribution …
Techno-economic analysis and energy forecasting study of domestic and commercial photovoltaic system installations in Estonia
The Baltic countries have good potential for solar photovoltaic (PV) energy generation, as on
average 15 hours of sunlight is available in summer. Another potential option is to …
average 15 hours of sunlight is available in summer. Another potential option is to …
[HTML][HTML] Exploratory data analysis based short-term electrical load forecasting: A comprehensive analysis
Power system planning in numerous electric utilities merely relies on the conventional
statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is …
statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is …
On wavelet transform based convolutional neural network and twin support vector regression for wind power ramp event prediction
HS Dhiman, D Deb, JM Guerrero - Sustainable Computing: Informatics and …, 2022 - Elsevier
Power produced from renewable energy sources carbon negative and promises an
increased reliability for grid integration. Wind energy sector globally has an installed …
increased reliability for grid integration. Wind energy sector globally has an installed …
[PDF][PDF] Short-term wind energy forecasting using deep learning-based predictive analytics
Wind energy is featured by instability due to a number of factors, such as weather, season,
time of the day, climatic area and so on. Furthermore, instability in the generation of wind …
time of the day, climatic area and so on. Furthermore, instability in the generation of wind …
State-of-the-Art Review of Emerging Trends in Renewable Energy Generation Technologies
Renewable energy generation sector has grown rapidly over the past decade with
expanding investments in relation to increasing political and public support, as well as …
expanding investments in relation to increasing political and public support, as well as …
[HTML][HTML] Predicting Energy Generation in Large Wind Farms: A Data-Driven Study with Open Data and Machine Learning
Wind energy has become a trend in Brazil, particularly in the northeastern region of the
country. Despite its advantages, wind power generation has been hindered by the high …
country. Despite its advantages, wind power generation has been hindered by the high …
[HTML][HTML] Short-term unit commitment by using machine learning to cover the uncertainty of wind power forecasting
Unit Commitment (UC) is a complicated integrational optimization method used in power
systems. There is previous knowledge about the generation that has to be committed among …
systems. There is previous knowledge about the generation that has to be committed among …