A novel temporal feature selection based LSTM model for electrical short-term load forecasting

K Ijaz, Z Hussain, J Ahmad, SF Ali, M Adnan… - IEEE …, 2022 - ieeexplore.ieee.org
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 …

Enhancing PV hosting capacity and mitigating congestion in distribution networks with deep learning based PV forecasting and battery management

N Shabbir, L Kütt, V Astapov, K Daniel, M Jawad… - Applied Energy, 2024 - Elsevier
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 …

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 …

Techno-economic analysis and energy forecasting study of domestic and commercial photovoltaic system installations in Estonia

N Shabbir, L Kütt, HA Raja, M Jawad, A Allik, O Husev - Energy, 2022 - Elsevier
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 …

[HTML][HTML] Exploratory data analysis based short-term electrical load forecasting: A comprehensive analysis

U Javed, K Ijaz, M Jawad, EA Ansari, N Shabbir, L Kütt… - Energies, 2021 - mdpi.com
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 …

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 …

[PDF][PDF] Short-term wind energy forecasting using deep learning-based predictive analytics

N Shabbir, L Kütt, M Jawad, O Husev… - Comput. Mater …, 2022 - researchgate.net
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 …

State-of-the-Art Review of Emerging Trends in Renewable Energy Generation Technologies

H Tiismus, V Maask, V Astapov, T Korõtko… - IEEE Access, 2025 - ieeexplore.ieee.org
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 …

[HTML][HTML] Predicting Energy Generation in Large Wind Farms: A Data-Driven Study with Open Data and Machine Learning

M Paula, W Casaca, M Colnago, JR da Silva, K Oliveira… - Inventions, 2023 - mdpi.com
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 …

[HTML][HTML] Short-term unit commitment by using machine learning to cover the uncertainty of wind power forecasting

D Salman, M Kusaf - Sustainability, 2021 - mdpi.com
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 …