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[HTML][HTML] Artificial intelligence techniques for enabling Big Data services in distribution networks: A review
Artificial intelligence techniques lead to data-driven energy services in distribution power
systems by extracting value from the data generated by the deployed metering and sensing …
systems by extracting value from the data generated by the deployed metering and sensing …
Advanced methods for photovoltaic output power forecasting: A review
Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into
the grid. The design of accurate photovoltaic output forecasters remains a challenging issue …
the grid. The design of accurate photovoltaic output forecasters remains a challenging issue …
Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast
In this paper, a forecasting algorithm is proposed to predict photovoltaic (PV) power
generation using a long short term memory (LSTM) neural network (NN). A synthetic …
generation using a long short term memory (LSTM) neural network (NN). A synthetic …
Artificial intelligence enabled demand response: Prospects and challenges in smart grid environment
Demand Response (DR) has gained popularity in recent years as a practical strategy to
increase the sustainability of energy systems while reducing associated costs. Despite this …
increase the sustainability of energy systems while reducing associated costs. Despite this …
Probabilistic day-ahead prediction of PV generation. A comparative analysis of forecasting methodologies and of the factors influencing accuracy
Photovoltaic (PV) power forecasting is essential for the integration of renewable energy
sources into the grid and for the optimisation of energy management systems. In this paper …
sources into the grid and for the optimisation of energy management systems. In this paper …
Evolution of microgrids with converter-interfaced generations: Challenges and opportunities
Although microgrids facilitate the increased penetration of distributed generations (DGs) and
improve the security of power supplies, they have some issues that need to be better …
improve the security of power supplies, they have some issues that need to be better …
Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning
The outputs of photovoltaic (PV) power are random and uncertain due to the variations of
meteorological elements, which may disturb the safety and stability of power system …
meteorological elements, which may disturb the safety and stability of power system …
PV power forecasting based on data-driven models: a review
Accurate PV power forecasting techniques are a prerequisite for the optimal management of
the grid and its stability. This paper presents a review of the recent developments in the field …
the grid and its stability. This paper presents a review of the recent developments in the field …
Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting
With the fast expansion of renewable energy system installed capacity in recent years, the
availability, stability, and quality of smart grids have become increasingly important. The …
availability, stability, and quality of smart grids have become increasingly important. The …
Energy forecasting: a comprehensive review of techniques and technologies
Distribution System Operators (DSOs) and Aggregators benefit from novel energy
forecasting (EF) approaches. Improved forecasting accuracy may make it easier to deal with …
forecasting (EF) approaches. Improved forecasting accuracy may make it easier to deal with …