Global Organization
This paper investigates solar PV power generation forecasting techniques presented to date and describes the characteristics of various forecasting techniques. These approaches are compared together in terms of forecast method, time horizon, measurement error, input and output variables, computational time, and benchmark model.
This study provides a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures. It critically analyzes recent studies on solar PV power forecasting to highlight the strengths and weaknesses of the techniques or models implemented.
Accurately forecasting PV power generation can reduce the effect of PV power uncertainty on the grid, improve system reliability, maintain power quality, and increase the penetration level of PV systems.
In addition, future studies should also consider the optimization of the PV power forecasting model. Most of the recent studies in this field have focused on investigating direct PV power forecasting. Direct forecasting methods can achieve accurate forecasting of PV power generation.
The model is often used as a benchmark for other techniques, as well as for short-term PV output power generation forecasting. The method involves applying statistical tools to analyze the different input variables for forecasting PV output power. Hence, historical time-series data are used in this method .
PV power generation forecasting is long-term by considering climatic data such as solar irradiance, temperature and humidity. Moreover, we implemented these deep learning methods on two datasets, the first one is made of electrical consumption data collected from smart meters installed at consumers in Douala.
XAI is extensively used in industry for vibration signal analysis [122], multivariate time series forecasting [99], industry machinery [123], solar power generation forecasting …
A thorough comparison of various forecasting techniques suggests that deep neural networks with ensemble technique or hybrid techniques supersedes the traditional …
In this chapter, the different methods used in PV power forecasting are presented, applied and their accuracy in PV forecasting is evaluated using measured PV …
2. Available PV power forecasting methodologies. Solar power forecasting can be divided into two groups- (1) Direct forecasting, that directly forecasts PV power. (2) Indirect forecasting, that is based on solar radiation forecasting. Several …
An accurate forecasting of the PV power generation can reduce the impact of …
Forecasting of large-scale renewable energy clusters composed of wind power generation, photovoltaic and concentrating solar power (CSP) generation encounters complex …
Abstract. Accurate forecasting of solar PV generation is critical for …
This study aims to present deep learning algorithms for electrical demand prediction and solar PV power generation forecasting. Therefore, we proposed a novel multi-objective hybrid model named FFNN …
As a result of this industrial revolution, solar photovoltaic (PV) systems have drawn much attention as a power generation source for varying applications, including the main utility-grid power ...
This review has outlined a pioneering, comprehensive framework for solar PV power generation prediction, addressing a critical need due to the intermittent and stochastic nature of RESs. This systematic …
In this chapter, the different methods used in PV power forecasting are presented, applied and their accuracy in PV forecasting is evaluated using measured PV module and weather data. The degradation …
The aim of this review paper is providing the necessary data about the basic principles and standards of photovoltaic (PV) power forecasting by stating numerous research …
Forecasting plays an indispensable role in the grid integration of solar energy, which is an important pathway toward the grand goal of achieving planetary carbon neutrality.
This paper aims to analyze and compare various methods of solar photovoltaic power forecasting in terms of characteristics and performance. This work classifies solar …
The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid …
This study aims to present deep learning algorithms for electrical demand prediction and solar PV power generation forecasting. Therefore, we proposed a novel multi …
We provide an overview of factors affecting solar PV power forecasting and an overview of existing PV power forecasting methods in the literature, with a specific focus on ML-based models.
A thorough comparison of various forecasting techniques suggests that …
This study provides a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures. It …
This paper aims to analyze and compare various methods of solar photovoltaic …
Renewable energy (RE) sources, such as wind, geothermal, bioenergy, and solar, have gained interest in developed regions. The rapid expansion of the economies in the …
This review has outlined a pioneering, comprehensive framework for solar PV power generation prediction, addressing a critical need due to the intermittent and stochastic …