scholarly journals A Short-Term Photovoltaic Power Generation Forecast Method Based on LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yang Li ◽  
Feng Ye ◽  
Zihao Liu ◽  
Zhijian Wang ◽  
Yupeng Mao
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Prediction Accuracy ◽  
Meteorological Data ◽  
Time Correlation ◽  
Support Vector ◽  
Continuous Data ◽  
Short Term ◽  
Photovoltaic Power ◽  
Periodic Data

The intermittence and fluctuation of photovoltaic power generation seriously affect output power reliability, efficiency, fault detection of photovoltaic power grid, etc. The precise forecasting of photovoltaic power generation is the critical method to solve the above limitations. Current photovoltaic power generation forecasting methods generally usually adopt meteorological data and historical continuous photovoltaic power generation as inputs, but they do not take into account historical periodic photovoltaic power generation as inputs, which makes the existing methods inadequate in learning time correlation. Therefore, to further study the time correlation for improving the prediction accuracy, an LSTM-FC deep learning algorithm composed of long-term short-term memory (LSTM) and fully connected (FC) layers is proposed. The double-branch input of the model enables it not only to consider the impact of meteorological data on power generation but also to consider time continuity and periodic dependence, thereby improving the prediction accuracy to a certain extent. In this paper, meteorological data, historical continuous data, and historical periodic data are used as experimental data, and these three types of data are combined into different input forms to evaluate and compare LSTM-FC with other baseline models, including support vector machines (SVM), gradient boosting decision tree (GBDT), generalized regression neural network (GRNN), feedforward neural network (FFNN), and LSTM. The simulation results show that the accuracy of the models with meteorological data, continuous data, and periodic data as input is higher than that of other input forms, and the accuracy of LSTM-FC is the highest among these models, and its root mean square error (RMSE) is 11.79% higher than that of SVM.

scholarly journals Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3247 ◽  
Author(s):  
Dongkyu Lee ◽  
Jinhwa Jeong ◽  
Sung Hoon Yoon ◽  
Young Tae Chae
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Recurrent Neural Network ◽  
Power Output ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Feature Engineering ◽  
Short Term ◽  
Interaction Detection ◽  
Photovoltaic Power

The time resolution and prediction accuracy of the power generated by building-integrated photovoltaics are important for managing electricity demand and formulating a strategy to trade power with the grid. This study presents a novel approach to improve short-term hourly photovoltaic power output predictions using feature engineering and machine learning. Feature selection measured the importance score of input features by using a model-based variable importance. It verified that the normative sky index in the weather forecasted data had the least importance as a predictor for hourly prediction of photovoltaic power output. Six different machine-learning algorithms were assessed to select an appropriate model for the hourly power output prediction with onsite weather forecast data. The recurrent neural network outperformed five other models, including artificial neural networks, support vector machines, classification and regression trees, chi-square automatic interaction detection, and random forests, in terms of its ability to predict photovoltaic power output at an hourly and daily resolution for 64 tested days. Feature engineering was then used to apply dropout observation to the normative sky index from the training and prediction process, which improved the hourly prediction performance. In particular, the prediction accuracy for overcast days improved by 20% compared to the original weather dataset used without dropout observation. The results show that feature engineering effectively improves the short-term predictions of photovoltaic power output in buildings with a simple weather forecasting service.

scholarly journals Short-Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Zhifeng Zhong ◽  
Chenxi Yang ◽  
Wenyang Cao ◽  
Chenyang Yan
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Prediction Accuracy ◽  
Particle Swarm Optimization Algorithm ◽  
Theory Model ◽  
Grey Model ◽  
Grey Theory ◽  
Short Term ◽  
Swarm Optimization ◽  
Photovoltaic Power

Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV) system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short-term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives.

scholarly journals A Combined Method of Two-model based on Forecasting Meteorological Data for Photovoltaic Power Generation Forecasting

2020 ◽  
Vol 185 ◽  
pp. 01053
Author(s):  
Xiao Zhang ◽  
Runjie Shen ◽  
Yiying Wang
Keyword(s):  
Power Generation ◽  
Output Power ◽  
Prediction Accuracy ◽  
Wavelet Decomposition ◽  
Meteorological Data ◽  
Complex Data ◽  
Combined Method ◽  
Model Based ◽  
Photovoltaic Power ◽  
Different Types

Under the background of the continuous development of photovoltaic power generation technology, accurate prediction of photovoltaic output power has become an important subject. In this paper, a combined method of two-model based on forecasting meteorological data for photovoltaic power generation forecasting is proposed. To solve the problem of the adaptability of a single model, two different models are used according to the different types of output power characteristics. The K-means clustering algorithm is used to classify different weather types according to the historical meteorological data. After predicting the irradiance and temperature of the period to be predicted and classifying the period into different types, the photovoltaic output power is predicted by a suitable model. The two prediction models are the Wavelet- Decomposition-ARIMA model and EDM-SA-DBN model, which are suitable for periods with larger and smaller fluctuation amplitude of photovoltaic output, respectively. Wavelet decomposition can refine the data with large fluctuations on multiple scales, make the data smooth, and improve the prediction accuracy of the Autoregressive Integrated Moving Average model (ARIMA). The Deep Belief Network (DBN) can effectively process a large number of complex data and deep mining the data features. While the empirical mode decomposition (EMD) can decompose the more stable data and amplify the details in the signal as much as possible. Meanwhile, the simulated annealing algorithm (SA) can avoid the network falling into a local optimal solution and improve the prediction accuracy. This paper uses a large number of photovoltaic power station data for experimental verification. The results show that this combined model has high accuracy and generalization ability.

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.

Sign in / Sign up

Export Citation Format

Share Document