Design of Photovoltaic Power Generation Prediction Model with Recurrent Neural Network

2019 ◽  
Vol 46 (6) ◽  
pp. 506-514
Author(s):  
Hanho Kim ◽  
Haesung Tak ◽  
Hwan-gue Cho
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Prediction Model ◽  
Recurrent Neural Network ◽  
Photovoltaic Power

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.

scholarly journals Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6460
Author(s):  
Dae-Yeon Kim ◽  
Dong-Sik Choi ◽  
Jaeyun Kim ◽  
Sung Wan Chun ◽  
Hyo-Wook Gil ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Glucose Level ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Percentage Error ◽  
Glucose Prediction

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

scholarly journals Applying a Cerebellar Model Articulation Controller Neural Network to a Photovoltaic Power Generation System Fault Diagnosis

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Bo-Jyun Liao ◽  
Chin-Pao Hung
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Fault Diagnosis ◽  
Cerebellar Model Articulation Controller ◽  
Training Process ◽  
Cmac Neural Network ◽  
Photovoltaic Power ◽  
Training Samples ◽  
Parallel Connections ◽  
Model Features

This study employed a cerebellar model articulation controller (CMAC) neural network to conduct fault diagnoses on photovoltaic power generation systems. We composed a module array using 9 series and 2 parallel connections of SHARP NT-R5E3E 175 W photovoltaic modules. In addition, we used data that were outputted under various fault conditions as the training samples for the CMAC and used this model to conduct the module array fault diagnosis after completing the training. The results of the training process and simulations indicate that the method proposed in this study requires fewer number of training times compared to other methods. In addition to significantly increasing the accuracy rate of the fault diagnosis, this model features a short training duration because the training process only tunes the weights of the exited memory addresses. Therefore, the fault diagnosis is rapid, and the detection tolerance of the diagnosis system is enhanced.

scholarly journals Photovoltaic Power Prediction Based on Scene Simulation Knowledge Mining and Adaptive Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Dongxiao Niu ◽  
Yanan Wei ◽  
Yanchao Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Power Prediction ◽  
Adaptive Neural Network ◽  
Scene Simulation ◽  
Knowledge Mining ◽  
Photovoltaic Power ◽  
Model Combining ◽  
Neural Network Prediction ◽  
Network Prediction

Influenced by light, temperature, atmospheric pressure, and some other random factors, photovoltaic power has characteristics of volatility and intermittent. Accurately forecasting photovoltaic power can effectively improve security and stability of power grid system. The paper comprehensively analyzes influence of light intensity, day type, temperature, and season on photovoltaic power. According to the proposed scene simulation knowledge mining (SSKM) technique, the influencing factors are clustered and fused into prediction model. Combining adaptive algorithm with neural network, adaptive neural network prediction model is established. Actual numerical example verifies the effectiveness and applicability of the proposed photovoltaic power prediction model based on scene simulation knowledge mining and adaptive neural network.

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