Deep learning (DL)-based PV Power Forecasting (PVPF) emerged nowadays as a
promising research direction to intelligentize energy systems. With the massive smart meter …

This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique
based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with …

A review of the state‐of‐the‐art in short‐term Solar Power Forecasting (SPF) methodologies
is presented in this paper. Over the last few years, developing and improving solar …

The rapid development of digital technologies and solutions is disrupting the energy sector.
In this regard, digitalization is a facilitator and enabler for integrating renewable energies …

Abstract Machine learning (ML) and three-dimensional (3D) printing are among the fastest-
growing branches of science. While ML can enable computers to independently learn from …

This paper proposes an effective deep learning framework for Short-Term Load Forecasting
(STLF) of multivariate time series. The proposed model consists of a hybrid Convolutional …

The random fluctuation and non-uniformity of Photovoltaic (PV) power generation greatly
affect the power grids' stability and operation. This paper addresses the high volatility of PV …

Due to the uncertainty of weather conditions and the nonlinearity of high-dimensional data,
as well as the need for a continuous and stable power supply to the power system …

Uncertainty in wind power is often unacceptably large and can easily affect the proper
operation, quality of generation, and economics of the power system. In order to mitigate the …

Renewable energy sources produce electricity without causing increment in pollution, and
solar energy is one of the primary renewable sources. Switching to renewable electricity is …