An Improved Method for Extracting Photovoltaic Module I–V Characteristic Curve Using Hybrid Learning Machine System

2021 ◽  
Vol 143 (5) ◽  
Author(s):  
Tamer Khatib ◽  
Rezeq Direya ◽  
Asmaa Said
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Solar Radiation ◽  
Hybrid Model ◽  
Photovoltaic Module ◽  
Random Forest Algorithm ◽  
Improved Method ◽  
Photovoltaic Modules ◽  
Proposed Model ◽  
Cascade Neural Network

Abstract This paper provides an improved method for predicting the I–V curve of the photovoltaic module using a hybrid machine learning system. The proposed method is based on a random forest algorithm and a cascade forward neural network. A random forest algorithm is used to predict a specific factor that is subsequently used as an input for the cascade neural network to remove the correlation between voltage and current. Then, the actual current is predicted using the cascade neural network. This procedure assures the ability of the proposed model to extract the I–V curve of any photovoltaic module regardless of its rating or type. A dataset that contains values for air temperature, solar radiation, voltage, and current of two polycrystalline photovoltaic modules is used in the training process of the proposed algorithm. The hybrid model has general inputs such as ambient temperature, solar radiation, and data from the photovoltaic module datasheet (Voc and Isc). The proposed model is trained, tested, and validated by 86% of the data. Meanwhile, 14% of the data are used for testing. Thus, the proposed model is tested using unknown data so as to avoid overfitting. Results show that the proposed model is very accurate in predicting I–V curves based on three types of errors which are mean absolute percentage error (0.68%), mean bias error (0.0191 A), and root-mean-squared error (0.04458 A). This hybrid model can be used to obtain the I–V curves for several types of photovoltaic modules.

Deep Convolution Neural Network and Random Forest Algorithm for BCI based Photo Imagery Learning

2021 ◽  
Author(s):  
C. Thirumarai Selvi ◽  
R. S. Sankarasubramanian ◽  
P. Gnana Prakash ◽  
R. Narendra Kumar ◽  
K. Chandra Mohan
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Convolution Neural Network ◽  
Random Forest Algorithm ◽  
Deep Convolution Neural Network

scholarly journals Analysis of the molecular nature associated with microsatellite status in colon cancer identifies clinical implications for immunotherapy

2020 ◽  
Vol 8 (2) ◽  
pp. e001437
Author(s):  
Xuanwen Bao ◽  
Hangyu Zhang ◽  
Wei Wu ◽  
Shaobing Cheng ◽  
Xiaomeng Dai ◽  
...  
Keyword(s):  
Neural Network ◽  
Colon Cancer ◽  
T Cells ◽  
T Cell ◽  
Random Forest ◽  
Microsatellite Instability ◽  
Gene Signature ◽  
Random Forest Algorithm ◽  
Related Gene ◽  
Molecular Nature

BackgroundMicrosatellite instability in colon cancer implies favorable therapeutic outcomes after checkpoint blockade immunotherapy. However, the molecular nature of microsatellite instability is not well elucidated.MethodsWe examined the immune microenvironment of colon cancer using assessments of the bulk transcriptome and the single-cell transcriptome focusing on molecular nature of microsatellite stability (MSS) and microsatellite instability (MSI) in colorectal cancer from a public database. The association of the mutation pattern and microsatellite status was analyzed by a random forest algorithm in The Cancer Genome Atlas (TCGA) and validated by our in-house dataset (39 tumor mutational burden (TMB)-low MSS colon cancer, 10 TMB-high MSS colon cancer, 15 MSI colon cancer). A prognostic model was constructed to predict the survival potential and stratify microsatellite status by a neural network.ResultsDespite the hostile CD8+ cytotoxic T lymphocyte (CTL)/Th1 microenvironment in MSI colon cancer, a high percentage of exhausted CD8+ T cells and upregulated expression of immune checkpoints were identified in MSI colon cancer at the single-cell level, indicating the potential neutralizing effect of cytotoxic T-cell activity by exhausted T-cell status. A more homogeneous highly expressed pattern of PD1 was observed in CD8+ T cells from MSI colon cancer; however, a small subgroup of CD8+ T cells with high expression of checkpoint molecules was identified in MSS patients. A random forest algorithm predicted important mutations that were associated with MSI status in the TCGA colon cancer cohort, and our in-house cohort validated higher frequencies of BRAF, ARID1A, RNF43, and KM2B mutations in MSI colon cancer. A robust microsatellite status–related gene signature was built to predict the prognosis and differentiate between MSI and MSS tumors. A neural network using the expression profile of the microsatellite status–related gene signature was constructed. A receiver operating characteristic curve was used to evaluate the accuracy rate of neural network, reaching 100%.ConclusionOur analysis unraveled the difference in the molecular nature and genomic variance in MSI and MSS colon cancer. The microsatellite status–related gene signature is better at predicting the prognosis of patients with colon cancer and response to the combination of immune checkpoint inhibitor–based immunotherapy and anti-VEGF therapy.

Hybrid Model for Prediction of Carbon Monoxide and Fine Particulate Matter Concentrations near a Road Intersection

2015 ◽  
Vol 2503 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Zhanyong Wang ◽  
Hong-Di He ◽  
Feng Lu ◽  
Qing-Chang Lu ◽  
Zhong-Ren Peng
Keyword(s):  
Neural Network ◽  
Carbon Monoxide ◽  
Particulate Matter ◽  
Air Quality ◽  
Hybrid Model ◽  
Urban Areas ◽  
Fine Particulate Matter ◽  
Principal Component ◽  
Fine Particulate ◽  
Proposed Model

Air quality time series near road intersections consist of complex linear and nonlinear patterns and are difficult to forecast. The backpropagation neural network (BPNN) has been applied for air quality forecasting in urban areas, but it has limited accuracy because of the inability to predict extreme events. This study proposed a novel hybrid model called GAWNN that combines a genetic algorithm and a wavelet neural network to improve forecast accuracy. The proposed model was examined through predicting the carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations near a road intersection. Before the predictions, principal component analysis was adopted to generate principal components as input variables to reduce data complexity and collinearity. Then the GAWNN model and the BPNN model were implemented. The comparative results indicated that GAWNN provided more reliable and accurate predictions of CO and PM2.5 concentrations. The results also showed that GAWNN performed better than BPNN did in the capability of forecasting extreme concentrations. Furthermore, the spatial transferability of the GAWNN model was reasonably good despite a degenerated performance caused by the unavoidable difference between the training and test sites. These findings demonstrate the potential of the application of the proposed model to forecast the fine-scale trend of air pollution in the vicinity of a road intersection.

scholarly journals Monitoring and Recognizing Enterprise Public Opinion from High-Risk Users Based on User Portrait and Random Forest Algorithm

Axioms ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 106
Author(s):  
Tinggui Chen ◽  
Xiaohua Yin ◽  
Lijuan Peng ◽  
Jingtao Rong ◽  
Jianjun Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Public Opinion ◽  
High Risk ◽  
Random Forest ◽  
Recognition Accuracy ◽  
Rapid Development ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Random Forest Algorithm ◽  
Negative Comments

With the rapid development of “We media” technology, netizens can freely express their opinions regarding enterprise products on a network platform. Consequently, online public opinion about enterprises has become a prominent issue. Negative comments posted by some netizens may trigger negative public opinion, which can have a significant impact on an enterprise’s image. From the perspective of helping enterprises deal with negative public opinion, this paper combines user portrait technology and a random forest algorithm to help enterprises identify high-risk users who have posted negative comments and thus may trigger negative public opinion. In this way, enterprises can monitor the public opinion of high-risk users to prevent negative public opinion events. Firstly, we crawled the information of users participating in discussions of product experience, and we constructed a portrait of enterprise public opinion users. Then, the characteristics of the portraits were quantified into indicators such as the user’s activity, the user’s influence, and the user’s emotional tendency, and the indicators were sorted. According to the order of the indicators, the users were divided into high-risk, moderate-risk, and low-risk categories. Next, a supervised high-risk user identification model for this classification was established, based on a random forest algorithm. In turn, the trained random forest identifier can be used to predict whether the authors of newly published public opinion information are high-risk users. Finally, a back propagation neural network algorithm was used to identify users and compared with the results of model recognition in this paper. The results showed that the average recognition accuracy of the back propagation neural network is only 72.33%, while the average recognition accuracy of the model constructed in this paper is as high as 98.49%, which verifies the feasibility and accuracy of the proposed random forest recognition method.

scholarly journals Study on the Estimation of Forest Volume Based on Multi-Source Data

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7796
Author(s):  
Tao Hu ◽  
Yuman Sun ◽  
Weiwei Jia ◽  
Dandan Li ◽  
Maosheng Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Hybrid Model ◽  
Prediction Accuracy ◽  
Volume Estimation ◽  
Support Vector ◽  
Estimation Models

We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data combined with ground survey data. Taking Larix olgensis, Pinus koraiensis, and Pinus sylvestris plantations in Mengjiagang forest farms as the research object, based on the Chinese Academy of Forestry LiDAR, charge-coupled device, and hyperspectral (CAF-LiTCHy) integrated system, we extracted the visible vegetation index, texture features, terrain factors, and point cloud feature variables, respectively. Random forest (RF), support vector regression (SVR), and an artificial neural network (ANN) were used to estimate forest volume. In the small-scale space, the estimation of sample plot volume is influenced by the surrounding environment as well as the neighboring observed data. Based on the residuals of these three machine learning models, OK interpolation was applied to construct new hybrid forest volume estimation models called random forest Kriging (RFK), support vector machines for regression Kriging (SVRK), and artificial neural network Kriging (ANNK). The six estimation models of forest volume were tested using the leave-one-out (Loo) cross-validation method. The prediction accuracies of these six models are better, with RLoo2 values above 0.6, and the prediction accuracy values of the hybrid models are all improved to different extents. Among the six models, the RFK hybrid model had the best prediction effect, with an RLoo2 reaching 0.915. Therefore, the machine learning method based on multi-source remote sensing factors is useful for forest volume estimation; in particular, the hybrid model constructed by combining machine learning and the OK method greatly improved the accuracy of forest volume estimation, which, thus, provides a fast and effective method for the remote sensing inversion estimation of forest volume and facilitates the management of forest resources.

scholarly journals Research on Forecasting of China's Monetary Policy Based on Random Forest Algorithm.pdf

2020 ◽  
Author(s):  
chuanxin qiu
Keyword(s):  
Neural Network ◽  
Monetary Policy ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
Bank Of China ◽  
Macroeconomic Indicators ◽  
Neural Network Algorithm

This paper uses the random forest algorithm model to quantify and predict the monetary policy of the People's Bank of China under the input of 16 indicators macroeconomic indicators. It is compared with three other machine learning algorithms (CART decision tree, support vector machine and neural network algorithm), discrete selection model and combined prediction model. The results show that the random forest algorithm shows better prediction accuracy in predicting the direction of the central bank's monetary policy.

scholarly journals Research on Forecasting of China's Monetary Policy Based on Random Forest Algorithm.pdf

2020 ◽  
Author(s):  
chuanxin qiu
Keyword(s):  
Neural Network ◽  
Monetary Policy ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Algorithm ◽  
Bank Of China ◽  
Macroeconomic Indicators ◽  
Neural Network Algorithm

This paper uses the random forest algorithm model to quantify and predict the monetary policy of the People's Bank of China under the input of 16 indicators macroeconomic indicators. It is compared with three other machine learning algorithms (CART decision tree, support vector machine and neural network algorithm), discrete selection model and combined prediction model. The results show that the random forest algorithm shows better prediction accuracy in predicting the direction of the central bank's monetary policy.

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