The Visual Display of Quantitative Information

Machine learning methods for solar radiation forecasting: A review

Review of photovoltaic power forecasting

Pattern recognition

To mitigate the impact of climate change and global warming, the use of renewable energies is increasing day by day significantly. A considerable amount of electricity is generated from renewable energy sources since the last decade. Among the potential renewable energies, photovoltaic (PV) has experienced enormous growth in electricity generation. A large number of PV systems have been installed in on-grid and off-grid systems in the last few years. The number of PV systems will increase rapidly in the future due to the policies of the government and international organizations, and the advantages of PV technology. However, the variability of PV power generation creates different negative impacts on the electric grid system, such as the stability, reliability, and planning of the operation, aside from the economic benefits. Therefore, accurate forecasting of PV power generation is significantly important to stabilize and secure grid operation and promote large-scale PV power integration. A good number of research has been conducted to forecast PV power generation in different perspectives. This paper made a comprehensive and systematic review of the direct forecasting of PV power generation. The importance of the correlation of the input-output data and the preprocessing of model input data are discussed. This review covers the performance analysis of several PV power forecasting models based on different classifications. The critical analysis of recent works, including statistical and machine-learning models based on historical data, is also presented. Moreover, the strengths and weaknesses of the different forecasting models, including hybrid models, and performance matrices in evaluating the forecasting model, are considered in this research. In addition, the potential benefits of model optimization are also discussed.

Forecasting of photovoltaic power generation and model optimization: A review

https://oscarperpinan.github.io/papers/Pinho.Perpinan.ea2014.pdf

PV power forecast using a nonparametric PV model

This study develops a proposal of method of calculation useful to estimate the energy produced by
a PV grid-connected system making use of irradiance-domain integrals and denition of statistical
moment. Validation against database of real PV plants performance data shows that acceptable
energy estimation can be obtained with rst to fourth statistical moments and some basic system
parameters. This way, only simple calculations at the reach of pocket calculators, are enough to
estimate AC energy.

/pdf/on-the-calculation-of-energy-produced-by-a-pv-grid-connected-5d3y3f6nww.pdf

On the calculation of energy produced by a PV grid-connected system

A review of existing studies about life cycle assessment (LCA) of PV systems has been carried out. The data from this review have been completed with our own figures in order to calculate the energy payback time (EPBT) of double and horizontal axis tracking and fixed systems. The results of this metric span from 2 to 5 years for the latitude and global irradiation ranges of the geographical area comprised between -10° to 10° of longitude, and 30° to 45° of latitude. With the caution due to the uncertainty of the sources of information, these results mean that a grid connected PV system (GCPVS) is able to produce back the energy required for its existence from 6 to 15 times during a life cycle of 30 years. When comparing tracking and fixed systems, the great importance of the PV generator makes advisable to dedicate more energy to some components of the system in order to increase the productivity and to obtain a higher performance of the component with the highest energy requirement. Both double axis and horizontal axis trackers follow this way, requiring more energy in metallic structure, foundations and wiring, but this higher contribution is widely compensated by the improved productivity of the system.

Energy Payback Time of Grid Connected PV Systems : Comparison Between Tracking and Fixed Systems

https://iopscience.iop.org/article/10.1088/1748-9326/ab6666/pdf

O. Perpiñán

Papers

PV power forecast using a nonparametric PV model

On the calculation of energy produced by a PV grid-connected system

Energy Payback Time of Grid Connected PV Systems : Comparison Between Tracking and Fixed Systems

Future evolution of surface solar radiation and photovoltaic potential in Europe: investigating the role of aerosols

Comparative study of PV power forecast using parametric and nonparametric PV models