Machine learning in medical applications: A review of state-of-the-art methods
Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …
complex challenges in recent years in various application areas, such as medical, financial …
[HTML][HTML] Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark
While the field of electricity price forecasting has benefited from plenty of contributions in the
last two decades, it arguably lacks a rigorous approach to evaluating new predictive …
last two decades, it arguably lacks a rigorous approach to evaluating new predictive …
Machine learning and deep learning in smart manufacturing: The smart grid paradigm
Industry 4.0 is the new industrial revolution. By connecting every machine and activity
through network sensors to the Internet, a huge amount of data is generated. Machine …
through network sensors to the Internet, a huge amount of data is generated. Machine …
[HTML][HTML] Electricity load forecasting: a systematic review
IK Nti, M Teimeh, O Nyarko-Boateng… - Journal of Electrical …, 2020 - Springer
The economic growth of every nation is highly related to its electricity infrastructure, network,
and availability since electricity has become the central part of everyday life in this modern …
and availability since electricity has become the central part of everyday life in this modern …
Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review
Meteorological changes urge engineering communities to look for sustainable and clean
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
[HTML][HTML] Prospects and challenges of the machine learning and data-driven methods for the predictive analysis of power systems: A review
W Strielkowski, A Vlasov, K Selivanov, K Muraviev… - Energies, 2023 - mdpi.com
The use of machine learning and data-driven methods for predictive analysis of power
systems offers the potential to accurately predict and manage the behavior of these systems …
systems offers the potential to accurately predict and manage the behavior of these systems …
Distributional neural networks for electricity price forecasting
We present a novel approach to probabilistic electricity price forecasting which utilizes
distributional neural networks. The model structure is based on a deep neural network …
distributional neural networks. The model structure is based on a deep neural network …
A survey on hyperparameters optimization algorithms of forecasting models in smart grid
Forecasting in the smart grid (SG) plays a vital role in maintaining the balance between
demand and supply of electricity, efficient energy management, better planning of energy …
demand and supply of electricity, efficient energy management, better planning of energy …
[HTML][HTML] Electricity theft detection using supervised learning techniques on smart meter data
Due to the increase in the number of electricity thieves, the electric utilities are facing
problems in providing electricity to their consumers in an efficient way. An accurate …
problems in providing electricity to their consumers in an efficient way. An accurate …
Dense skip attention based deep learning for day-ahead electricity price forecasting
The forecasting of the day-ahead electricity price (DAEP) has become more of interest to
decision makers in the liberalized market, as it can help optimize bidding strategies and …
decision makers in the liberalized market, as it can help optimize bidding strategies and …