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This article was submitted to Process and Energy Systems Engineering, a section of the journal Frontiers in Energy Research

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

For decades, solar energy has taken an increasingly important part, which will continue to rise, driven by carbon peaking and carbon neutrality strategic goals, in the energy consumption of China (^{1} power generation is more erratic than conventional power which results in some problems of the grid: frequency instability (

According to the modeling means of prediction, the prevailing PV power prediction methods are broadly divided into three categories, namely, physical, statistical, and artificial intelligence (AI) forecasting technologies (

Prediction algorithm and its corresponding time/space horizon.

The physical prediction method refers to a technology that excavates the factors related to PV power generation from the principle and then creates a physical model. Specifically, physical method modeling is based on numerical weather prediction (NWP) by utilizing atmospheric physical data including wind speed, temperature, rainfall, humidity, length of day (

NWP models which can be classified into two categories of wide-area prediction models and local area models prediction are utilized to forecast the solar illumination intensity and cloud distribution. Local area models are usually used for short-term forecasting of the PV plant power. So far, NAM (

Under the condition of reasonable model parameters, the physical PV power forecasting method can accurately predict the results of the future power output. However, the physical forecasting approach has the disadvantage of requiring a complex model of the solar radiation output and a characteristic model of the PV power generation system, as well as the precise future weather forecast information. In addition, determining the parameter values of the output characteristic model is more complicated for different types of generating unit systems (

The statistical method needs to collect a large number of data related to the power output of the PV power generation system to regress some unknown constants and further obtain the functional relationship between the output power and the measurable unknown. According to the amount of unknowns, the statistical method can be divided into the unary linear regression method, multiple linear regression method, and nonlinear regression method. Because there are many factors affecting PV system power generation, the prediction result is not satisfactory by using the unary linear regression method. The multiple linear regression method adopted in the literature (

NARX and NARMAX (

These aforementioned regression prediction models try to modify the models through the deviation between the measured and predicted values of PV power generation. In particular, the multiple linear regression method can enhance the prediction accuracy without extra measurement data, which is a method worthy of further study. The merits of the statistical method are simple operation, fast prediction, and good relation expression between the factors and the output power, hence more suitable for fitting the new situation. However, the statistical method has the complexity and difficulty in establishing the regression equation due to its high accuracy demand of the distribution rule and historical sample data. Thus, it has a lower prediction accuracy.

Nowadays, PV power forecasting based on the AI algorithm is a very popular research area because of its strong self-learning and self-adaptation ability. In the literature (

The efficient PV power forecasting technology can not only improve the grid connection ability and security but also effectively reduce light discarding. Also, various prediction technologies of the aforementioned PV plant are summarized and evaluated in

Classification of PV power prediction methods.

Basis of classification | Prediction methods | Definition | Characteristic |
---|---|---|---|

Forecasting process | Direct prediction method | Direct prediction based on historical PV power data | Suitable for cases of sufficient historical PV power data; difficult for modeling |

Indirect prediction method | Power forecasting combined with the correlation model based on the solar irradiance prediction of PV panels | Suitable for cases of lacking historical data but available solar irradiance and temperature historical data | |

Time scale | Short-term prediction | PV power forecasting of 1 day | Providing power variation information in short-term; used to make the power market scheduling plan |

Medium- and long-term prediction | PV power forecasting of 1 day to 1 year | Forecasting PV power information for a long term in the future | |

Spatial scale | Single-plant prediction method | Power prediction for a single PV system | Applied to the optimal operation of the PV system |

Regional prediction method | Power prediction for all PV systems in an area | Helpful for dispatching departments to predict the fluctuation of PV power | |

Modeling method | Physical prediction method | Power prediction by the power calculation model based on the solar altitude angle, geographic location, temperature, and solar irradiance etc. | Complex models; highly depend on reasonable parameters |

Statistical prediction method | Power prediction based on the statistical relationship between input and output data of the prediction model | Not require physical internal information of the PV plant | |

AI prediction method | Training the prediction model by AI algorithms with sample data | Strong self-learning and self-adaptation ability; need lots of historical data |

But the PV power forecasting technology still faces many challenges. Recommendations and limitations for future research studies are shown as follows:

1) Pre-processing of the mass of experimental data is manually performed; hence, efficient algorithms should be developed to effectively summarize and extract information data and establish connections among them;

2) It is urgent for developing a swarm intelligence algorithm to train the neural network model of PV power prediction;

3) Regional prediction is important for power dispatching which should be further analyzed and studied;

4) Many works only consider cloud cover as a meteorological factor to represent the extent of sky cover but ignore that the partial shading of the PV panel caused by a cloud will lead to the multi-peak phenomenon of the PV curve. This issue requires further research for more accurate power prediction.

HY: writing the original draft and editing. BY: conceptualization. YH: visualization and contributed to the discussion of the topic. NC: formal analysis.

NC was employed by China Southern Power Grid EHV Transmission Company.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.