Systematic literature review of photovoltaic output power forecasting

Abstract

Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing. Among the potential renewable energy sources, photovoltaic (PV) system installations keep continuously increasing world‐wide due to its economic and environmental contributions. Despite its significant benefits, the inherent variability of PV power generation due to meteorological parameters can cause power management/planning problems. Thus, forecasting of PV output data (directly or indirectly) in an accurate manner is a critical task to provide stability, reliability, and optimisation of the grid systems. In considering the literature reviewed, there are various research items utilizing PV output power forecasting. In this study, a systematic literature review based on the search of primary studies (published between 2010 and 2020), which forecast PV power generation using machine learning and deep learning methods, is reported. The studies are evaluated based on the PV material used, their approaches, generated outputs, data set used, and the performance evaluation methods. As a result, gaps and improvable points in the existing literature are revealed, and suggestions which include novelties are offered for future works.

An Optimal Algorithm for Renewable Energy Generation Based on Neural Network

“…On the basis of checking the cloud clusters, thin clouds and thick clouds are further extracted. Thick cloud (1) The feature point matching pairs whose number of intersection points with other matching pairs is greater than 3 are eliminated from the matching pair set. (2) Sort the remaining feature point matching pairs in an ascending order according to the ratio ðD ij /D ij′ Þ to form a feature point matching pair set P. Among them, D ij is the minimum value of the Euclidean distance in the feature point matching pair; D ij′ is the Euclidean distance of the remaining point pairs.…”

“…With the continuous improvement of the level of solar energy development and utilization, the proportion of photovoltaics connected to the grid is increasing. Due to the instability of the output of large-scale photovoltaics, the grid connection is easy to cause fluctuations in grid voltage, current, and frequency, affecting the power quality of the grid [1]. In order to eliminate the above-mentioned adverse effects, it is particularly important to improve the prediction accuracy of photovoltaic power.…”

A hybrid method for day‐ahead photovoltaic power forecasting based on generative adversarial network combined with convolutional autoencoder

“…The PV power forecasting methods have been variously investigated and analyzed [4][5][6][7] in recent decades. The most popular forecasting methods can be categorized into three types: the statistical time-series based methods, machine learning methods and the hybrid methods.…”

Scalable multi‐site photovoltaic power forecasting based on stream computing

“…However, since PV power generation is largely influenced by weather and climate, PV output has a very pronounced fluctuating characteristic, so the high proportion of distributed PV access brings serious challenges to power system operation and control. PV forecasting can supply indicative information to facilitate power generation management via control or energy dispatch, consequently promoting the stability and efficiency of the power grid's operation [5].…”