Photovoltaic power generation is highly valued and has developed rapidly throughout the
world. However, the fluctuation of solar irradiance affects the stability of the photovoltaic …

This paper aims to develop the long short-term memory (LSTM) network modelling strategy
based on deep learning principles, tailored for the very short-term, near-real-time global …

In response to the critical need for sustainable energy solutions, this study introduces a
groundbreaking hybrid model for enhancing solar radiation forecasting, which is crucial for …

Prediction of power output plays a vital role in the installation and operation of photovoltaic
modules. In this paper, two photovoltaic module technologies, amorphous silicon and …

There are incredibly renewable outlets such as solar and wind intermittent, and machine
reliability is hard to sustain with the intolerable balance of green energy resources. Because …

Mesoscale numerical weather prediction models are frequently utilized for wind speed
analysis and forecasting in the planning and support of wind power generation. However …

This paper proposes a new Hybrid Deep Neural Network (HDNN) based fusion method to
predict short-term solar photovoltaic (PV) power output. The HDNN is the combination of …

This paper aims to forecast solar power in very short horizons to assist in real-time
distribution system operations. Popular machine learning methods for time series …

Abstract Globally, Solar Power (SP) is generated by employing Photovoltaic (PV) systems.
Accurate forecasting of PV power is a critical issue in ensuring secure operation along with …

In this paper, we propose an implementation of a new technique of power maximization
using a photovoltaic system emulator. The PV system design and its performance evaluation …