scholarly journals Research on Photovoltaic power prediction technology Based on Machine Learning

2021 ◽  
Vol 2087 (1) ◽  
pp. 012004
Author(s):  
Hongxia Li ◽  
Jianlin Li ◽  
Yang Mi
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Prediction Method ◽  
Meteorological Factor ◽  
Power Prediction ◽  
Advantages And Disadvantages ◽  
Photovoltaic Power ◽  
Huge Impact ◽  
Basic Technology ◽  
Safety And Stability

Abstract In recent years, the photovoltaic power generation has obvious intermittent, randomness and volatility, and high permeability photovoltaic will have a huge impact on the safety and stability of the grid. The prediction of photovoltaic power generation is to improve the quality of photovoltaic grid, optimize grid scheduling, and ensure the basic technology of power grid safety and stability. In order to improve the prediction accuracy of photovoltaic power generation, this article will comprehensively carding and compare from 3 dimensions: photovoltaic power generation and meteorological factor correlation analysis, similar day selection, prediction method based on machine learning, and summarize the advantages and disadvantages of various methods. Further research has been put forward accordingly.

scholarly journals Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques

2019 ◽  
Vol 13 (7) ◽  
pp. 1009-1023 ◽  
Author(s):  
Muhammad Naveed Akhter ◽  
Saad Mekhilef ◽  
Hazlie Mokhlis ◽  
Noraisyah Mohamed Shah
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Photovoltaic Power ◽  
Metaheuristic Techniques

A state-of-the-art review on wind power deterministic prediction

2020 ◽  
pp. 0309524X2094120 ◽  
Author(s):  
Zhongda Tian
Keyword(s):  
Wind Power ◽  
Prediction Method ◽  
Scale Model ◽  
Ensemble Prediction ◽  
Prediction Methods ◽  
Power Prediction ◽  
Continuous Growth ◽  
Advantages And Disadvantages ◽  
Wind Power Prediction ◽  
The Impact

With the continuous growth of wind power access capacity, the impact of intermittent and volatile wind power generation on the grid is becoming more and more obvious, so the research of wind power prediction method has been widely concerned. Accurate wind power prediction can provide necessary support for the power grid dispatching, combined operation of generating units, operation, and maintenance of wind farms. According to the existing wind power prediction methods, the wind power prediction methods are systematically classified according to the time scale, model object, and model principle of prediction. The physical methods, statistical methods include single and ensemble prediction methods related to wind power prediction are introduced in detail. The error evaluation indicator of the prediction method is analyzed, and the advantages and disadvantages of each prediction method and its applicable occasions are given. At the same time, in view of the existing problems in the wind power prediction method, the corresponding improvement plan is put forward. Finally, this article points out that the research is needed for wind power prediction in the future.

scholarly journals Forecast of photovoltaic power generation based on DBSCAN

2021 ◽  
Vol 236 ◽  
pp. 02016
Author(s):  
Jiaying Zhang ◽  
Yingfan Zhang
Keyword(s):  
Power Generation ◽  
Prediction Model ◽  
Power Plants ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Renewable Energy Sources ◽  
Stable Operation ◽  
Power Prediction ◽  
Photovoltaic Power ◽  
Bidirectional Lstm

The power output of the photovoltaic power generation has prominent intermittent fluctuation characteristics. Large-scale photovoltaic power generation access will bring a specific impact on the safe and stable operation of the power grid. With the increase in the proportion of renewable energy sources such as wind power and photovoltaics, the phenomenon of wind abandonment and light abandonment has further increased. The photovoltaic power generation prediction is one of the critical technologies to solve this problem. It is of outstanding academic and application value to research photovoltaic power generation prediction methods and systems. Therefore, accurately carrying out the power forecast of photovoltaic power plants has become a research hot point in recent years. It is favored by scholars at home and abroad. First, this paper builds a simulation model of the photovoltaic cell based on known theoretical knowledge. Then it uses the density clustering algorithm (DBSCAN) in the clustering algorithm and classifies the original data. Finally, according to a series of problems such as the slow modeling speed of photovoltaic short-term power prediction, the bidirectional LSTM photovoltaic power prediction model, and CNN-GRU photovoltaic power prediction model based on clustering algorithm are proposed. After comparing the two models, it is concluded that the bidirectional LSTM prediction model is more accurate.

A Prediction Method for Daily Photovoltaic Power Generation based on Datamining

2014 ◽  
Vol 134 (10) ◽  
pp. 849-855 ◽  
Author(s):  
Hiroshi Sugimura ◽  
Bin Rin ◽  
Takeaki Mori
Keyword(s):  
Power Generation ◽  
Prediction Method ◽  
Photovoltaic Power

Photovoltaic Power Generation Data Preprocessing Using Wavelet Thresholding

2014 ◽  
Vol 936 ◽  
pp. 2179-2183
Author(s):  
Kai Bai ◽  
Hong Da Qu ◽  
Jin Zong ◽  
Zhi Li ◽  
Hua Xin Zhai ◽  
...  
Keyword(s):  
Power Generation ◽  
Historical Data ◽  
Data Preprocessing ◽  
Wavelet Thresholding ◽  
Two Dimensional ◽  
Grayscale Image ◽  
Power Prediction ◽  
Data Set ◽  
Photovoltaic Power ◽  
The Matrix

The accuracy of power prediction of photovoltaic power generation will be reduced if there are noises in the historical data of photovoltaic power generation. It is necessary to eliminate noises in the historical data. First, the power generation can be arranged in a two-dimensional data set by date. Secondly, the grayscale image matrix will be obtained after data normalized. Thirdly, two-dimensional wavelet based image de-noising method will be used in the matrix de-noising. Finally the real historical data will be obtained through anti-normalization the matrix which is already de-noised. We will analyze its results feasible and effectiveness.

Improved Golden Section Application in Maximum Power Point Tracking of Photovoltaic Power Generation

2014 ◽  
Vol 953-954 ◽  
pp. 52-56
Author(s):  
Wei Du ◽  
Wei Han ◽  
You Fei Tan
Keyword(s):  
Power Generation ◽  
Golden Section ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Advantages And Disadvantages ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Photovoltaic Power ◽  
Power Point

In order to increase the output power of the photovoltaic system, the maximum power point tracking is needed. As the starting point of research in the output nonlinear characteristics, analysis of the advantages and disadvantages of the conventional algorithm and have the poor dynamic and steady-state performance of the maximum power point tracking(MPPT), the improvement method of golden section(IGSS) is applied to the photovoltaic power generation system. The results indicate that the method can quickly track the maximum power point of photovoltaic cells.

scholarly journals Best Practices on Metamodel-Based Photovoltaic Monitoring System with Prediction Method for Photovoltaic Power Generation

2020 ◽  
Vol 10 (14) ◽  
pp. 4762
Author(s):  
Woo Sung Jang ◽  
Je Seong Hong ◽  
Jang Hwan Kim ◽  
Byung Kook Jeon ◽  
R. Young Chul Kim
Keyword(s):  
Power Generation ◽  
Solar Energy ◽  
Real Time ◽  
Monitoring System ◽  
Prediction Method ◽  
Heterogeneous Data ◽  
Photovoltaic System ◽  
Small Farm ◽  
Energy Company ◽  
Photovoltaic Power

HS Solar Energy Company Inc. in Sejong city, Korea, has a big problem on how to monitor heterogeneous inverters with different protocols. Still a current photovoltaic power plant with different inverters, it has attracted significant attention to its experience of difficulties in monitoring integrated power generation. To solve this problem for the company, we adapt a metamodel mechanism to easily manage and integrate heterogeneous data into a metamodel-based data format. The existing metamodel-based photovoltaic monitoring system (M-PVMS) of the HS solar energy company also needs to simply predict the photovoltaic power generation in a day for small farm owners in the countryside. Therefore, we propose a method for predicting the power generation of M-PVMS panels using the gated recurrent unit (GRU) algorithm, which supports real-time learning to predict the photovoltaic system behavior that rapidly accumulates data in real time. As a result, we can predict the power generation for small farm owners with a probability of 96.353%.

Sign in / Sign up

Export Citation Format

Share Document