scholarly journals Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation

2020 ◽  
Vol 185 ◽  
pp. 01052
Author(s):  
Runjie Shen ◽  
Ruimin Xing ◽  
Yiying Wang ◽  
Danqiong Hua ◽  
Ming Ma
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Random Forest ◽  
Prediction Model ◽  
Power Plants ◽  
Time Series Prediction ◽  
Cloud Amount ◽  
Continuous Model ◽  
Short Term ◽  
Photovoltaic Power

As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model.

scholarly journals Comparison Between Random Forest and Recurrent Neural Network for Photovoltaic Power Forecasting

2021 ◽  
Vol 43 (5) ◽  
pp. 347-355
Author(s):  
Ramek Kim ◽  
Kyungmin Kim ◽  
Johng-Hwa Ahn
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Random Forest ◽  
Recurrent Neural Network ◽  
Power Plants ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Cloud Amount ◽  
Coefficient Of Determination ◽  
Photovoltaic Power

Objectives : Photovoltaic power generation which significantly depends on meteorological conditions is intermittent and unstable. Therefore, accurate forecasting of photovoltaic power generation is a challenging task. In this research, random forest (RF), recurrent neural network (RNN), long short term memory (LSTM), and gated recurrent unit (GRU) are proposed and we will find an efficient model for forecasting photovoltaic power generation of photovoltaic power plants.Methods : We used photovoltaic power generation data from photovoltaic power plants at Gamcheonhang-ro, Saha-gu, Busan, and meteorological data from Busan Regional Meteorological Administration. We used solar irradiance, temperature, atmospheric pressure, humidity, wind speed, wind direction, duration of sunshine, and cloud amount as input variables. By applying the trial and error method, we optimized hyperparameters such as estimators in RF, and number of hidden layers, number of nodes, epochs, and validation split in RNN, LSTM, and GRU. We compared proposed models by evaluation indexes such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE).Results and Discussion : The best RF at 1,000 of number of decision tree achieved test R2=0.865, test RMSE=16.013, and test MAE=9.656. The best choice of RNN was 6 hidden layers and the number of nodes in each layer was 90. We set the epochs at 450. RNN achieved test R2=0.942, test RMSE=10.530, and test MAE=6.390. To find the best result of LSTM, we used 3 hidden layers, and the number of nodes was 600. The epochs were set to 200. LSTM achieved test R2=0.944, test RMSE=10.29, and test MAE=6.360. GRU was set to 3 hidden layer and the number of nodes was 450. The epochs were set to 500. GRU achieved test R2=0.945, test RMSE=10.189, and test MAE=5.968.Conclusions : We found RNN, LSTM, and GRU performed better than RF, and GRU model showed the best performance. Therefore, GRU is the most efficient model to predict photovoltaic power generation in Busan, Korea.

Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study

2020 ◽  
Vol 93 ◽  
pp. 106389 ◽  
Author(s):  
Dongxiao Niu ◽  
Keke Wang ◽  
Lijie Sun ◽  
Jing Wu ◽  
Xiaomin Xu
Keyword(s):  
Power Generation ◽  
Feature Selection ◽  
Random Forest ◽  
Short Term ◽  
Photovoltaic Power

scholarly journals Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guorong Zhu ◽  
Sha Peng ◽  
Yongchang Lao ◽  
Qichao Su ◽  
Qiujie Sun
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Prediction Model ◽  
Time Series Prediction ◽  
Electricity Consumption ◽  
Operating Efficiency ◽  
Short Term ◽  
Model Based ◽  
Consumption Data ◽  
Consumption Prediction

Short-term electricity consumption data reflects the operating efficiency of grid companies, and accurate forecasting of electricity consumption helps to achieve refined electricity consumption planning and improve transmission and distribution transportation efficiency. In view of the fact that the power consumption data is nonstationary, nonlinear, and greatly influenced by the season, holidays, and other factors, this paper adopts a time-series prediction model based on the EMD-Fbprophet-LSTM method to make short-term power consumption prediction for an enterprise's daily power consumption data. The EMD model was used to decompose the time series into a multisong intrinsic mode function (IMF) and a residual component, and then the Fbprophet method was used to predict the IMF component. The LSTM model is used to predict the short-term electricity consumption, and finally the prediction value of the combined model is measured based on the weights of the single Fbprophet and LSTM models. Compared with the single time-series prediction model, the time-series prediction model based on the EMD-Fbprophet-LSTM method has higher prediction accuracy and can effectively improve the accuracy of short-term regional electricity consumption prediction.

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 Time Series Prediction Model Based on Long Short-Term Memory Networks

2021 ◽  
Author(s):  
Linkai Wang ◽  
Jing Chen ◽  
Wei Wang ◽  
Ruofan Wang ◽  
Lina Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Short Term ◽  
Term Memory ◽  
Model Based ◽  
Long Short Term Memory

scholarly journals The Impact of Data Filtration on the Accuracy of Multiple Time-Domain Forecasting for Photovoltaic Power Plants Generation

2020 ◽  
Vol 10 (22) ◽  
pp. 8265
Author(s):  
Stanislav A. Eroshenko ◽  
Alexandra I. Khalyasmaa ◽  
Denis A. Snegirev ◽  
Valeria V. Dubailova ◽  
Alexey M. Romanov ◽  
...  
Keyword(s):  
Power Generation ◽  
Time Domain ◽  
Power Plants ◽  
Multiple Time ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Photovoltaic Power ◽  
Operational Forecasting ◽  
Short Term Forecasting ◽  
Forecasting Errors

The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days’ accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close scrutiny the main sources of photovoltaic power plants’ generation forecasting errors. The effectiveness of the empirical approach versus unsupervised learning was investigated in application to source data filtration in order to improve the power generation forecasting accuracy for unstable weather conditions. The k-nearest neighbors’ methodology was justified to be optimal for initial data filtration, based on the clusterization results, associated with peculiar weather and seasonal conditions. The photovoltaic power plants’ forecasting accuracy improvement was further investigated for a one hour-ahead time-domain. It was proved that operational forecasting could be implemented based on the results of short-term day-ahead forecast mismatches predictions, which form the basis for multiple time-domain integrated forecasting tools. After a comparison of multiple time series forecasting approaches, operational forecasting was realized based on the second-order autoregression function and applied to short-term forecasting errors with the resulting accuracy of 87%. In the concluding part of the article the authors from the points of view of computational efficiency and scalability proposed the hardware system composition.

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