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Application of big data and machine learning in smart grid, and associated security concerns: A review
This paper conducts a comprehensive study on the application of big data and machine
learning in the electrical power grid introduced through the emergence of the next …
learning in the electrical power grid introduced through the emergence of the next …
Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: A mini review
Site suitability problems in renewable energy studies have taken a new turn since the
advent of geographical information system (GIS). GIS has been used for site suitability …
advent of geographical information system (GIS). GIS has been used for site suitability …
Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine
Recently, many machine learning techniques have been successfully employed in
photovoltaic (PV) power output prediction because of their strong non-linear regression …
photovoltaic (PV) power output prediction because of their strong non-linear regression …
Deep learning based ensemble approach for probabilistic wind power forecasting
Due to the economic and environmental benefits, wind power is becoming one of the more
promising supplements for electric power generation. However, the uncertainty exhibited in …
promising supplements for electric power generation. However, the uncertainty exhibited in …
Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems
The power output (PO) of a photovoltaic (PV) system is highly variable because of its
dependence on solar irradiance and other meteorological factors. Hence, accurate PO …
dependence on solar irradiance and other meteorological factors. Hence, accurate PO …
Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression
Accurate forecasting of wind power plays an important role in an effective and reliable power
system. However, the fact of non-schedulability and fluctuation of wind power significantly …
system. However, the fact of non-schedulability and fluctuation of wind power significantly …
Single-hidden layer neural networks for forecasting intermittent demand
Managing intermittent demand is a vital task in several industrial contexts, and good
forecasting ability is a fundamental prerequisite for an efficient inventory control system in …
forecasting ability is a fundamental prerequisite for an efficient inventory control system in …
Randomization-based machine learning in renewable energy prediction problems: Critical literature review, new results and perspectives
In the last few years, methods falling within the family of randomization-based machine
learning models have grasped a great interest in the Artificial Intelligence community, mainly …
learning models have grasped a great interest in the Artificial Intelligence community, mainly …
Extreme learning machine based prediction of daily dew point temperature
The dew point temperature is a significant element particularly required in various
hydrological, climatological and agronomical related researches. This study proposes an …
hydrological, climatological and agronomical related researches. This study proposes an …
A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation
In this paper, the extreme learning machine (ELM) is employed to predict horizontal global
solar radiation (HGSR). For this purpose, the capability of developed ELM method is …
solar radiation (HGSR). For this purpose, the capability of developed ELM method is …