In recent years, the terms “Smart Energy” and “Smart Energy Systems” have been used to
express an approach that reaches broader than the term “Smart grid”. Where Smart Grids …

Variability of solar resource poses difficulties in grid management as solar penetration rates
rise continuously. Thus, the task of solar power forecasting becomes crucial to ensure grid …

In this paper an artificial neural network for photovoltaic plant energy forecasting is proposed
and analyzed in terms of its sensitivity with respect to the input data sets. Furthermore, the …

The main purpose of this work is to lead an assessment of the day ahead forecasting activity
of the power production by photovoltaic plants. Forecasting methods can play a fundamental …

This study presents a novel approach to detect the city-wide solar potential which utilizes
image segmentation with deep learning technology unlike traditional methods. In order to …

The accurate forecasting of energy production from renewable sources represents an
important topic also looking at different national authorities that are starting to stimulate a …

The relevance of forecasting in renewable energy sources (RES) applications is increasing,
due to their intrinsic variability. In recent years, several machine learning and hybrid …

The impact of wind turbines and photovoltaic systems on network operation and power
quality (harmonics, and voltage fluctuations) is very important. The capability of the power …

Forecasting models for photovoltaic energy production are important tools for managing
energy flows. The aim of this study was to accurately predict the energy production of a PV …

In this paper a forecasting method for the Next Day's energy production forecast is proposed
with respect to photovoltaic plants. A new hybrid method PHANN (Physical Hybrid Artificial …