scholarly journals Short-Term Photovoltaic Generation Forecasting Based on LVQ-PSO-BP Neural Network and Markov Chain Method

2019 ◽  
Vol 1267 ◽  
pp. 012083
Author(s):  
Xiran Wang ◽  
Xue Ma ◽  
Suhua Lou ◽  
Fei Peng ◽  
Song Wu
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Bp Neural Network ◽  
Short Term ◽  
Photovoltaic Generation ◽  
Markov Chain Method

scholarly journals Real Time Short-term Forecasting Method of Remaining Parking Space in Urban Parking Guidance Systems

2018 ◽  
Vol 30 (2) ◽  
pp. 173-185 ◽  
Author(s):  
Xiaobo Zhu ◽  
Jianhua Guo ◽  
Wei Huang ◽  
Fengquan Yu ◽  
Byungkyu Brian Park
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
Real Time ◽  
Parking Space ◽  
Short Term ◽  
Markov Chain Method ◽  
Proposed Model ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Urban Parking

Short-term forecasting of the remaining parking space is important for urban parking guidance systems (PGS). The previous methods like polynomial equations and neural network methods are difficult to be applied in practice because of low accuracy or lengthy initial training time which is unfavourable if real-time training is carried out on adapting to changing traffic conditions. To forecast the remaining parking space in real-time with higher accuracy and improve the performances of PGS, this study develops an online forecasting model based on a time series method. By analysing the characteristics of data collected in Nanjing, China, an autoregressive integrated moving average (ARIMA) model has been established and a real-time forecasting procedure developed. The performance of this proposed model has been further analysed and compared with the performances of a neural network method and the Markov chain method. The results indicate that the mean error of the proposed model is about 2 vehicles per 15 minutes, which can meet the requirements for general PGS. Furthermore, this method outperforms the neural network model and the Markov chain method both in individual and collective error analysis. In summary, the proposed online forecasting method appears to be promising for forecasting the remaining parking space in supporting the PGS.

Wind Power Forecasting Based on the BP Neural Network

2013 ◽  
Vol 341-342 ◽  
pp. 1303-1307 ◽  
Author(s):  
Jian Dong Mao ◽  
Xiao Jing Zhang ◽  
Juan Li
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Engineering Application ◽  
Weather Forecast ◽  
Difficult Problem ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

Accurate short-term wind power forecasting has important significance to safety, stability and economy of power system dispatching and also it is a difficult problem in practical engineering application. In this paper, by use of the data of numerical weather forecast, such as wind speed, wind direction, temperature, relative humidity and pressure of atmosphere, a short-term wind power forecasting system based on BP neural network has been developed. For verifying the feasibility of the system, some experiments have been were carried out. The results show that the system is capable of predicting accurately the wind power of future 24 hours and the forecasting accuracy of 85.6% is obtained. The work of this paper has important engineering directive significance to the similar wind power forecasting system.

Wind Power Short-Term Forcasting of BP Neural Network Based on the Small-World Optimization

2014 ◽  
Vol 933 ◽  
pp. 384-389
Author(s):  
Xin Zhao ◽  
Shuang Xin Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Wind Direction ◽  
Wind Farm ◽  
Prediction Method ◽  
Small World ◽  
Power Method ◽  
Short Term

Wind power short-term forcasting of BP neural network based on the small-world optimization is proposed. First, the initial data collected from wind farm are revised, and the unreasonable data are found out and revised. Second, the small-world optimization BP neural network model is proposed, and the model is used on the prediction method of wind speed and wind direction, and the prediction method of power. Finally, by simulation analysis, the NMAE and NRMSE of the power method are smaller than those of the wind speed and wind direction method when the wind power data of one hour later are predicted. When the power method are used to forecast the data one hour later, NMAE is 5.39% and NRMSE is 6.98%.

scholarly journals The Daily Container Volumes Prediction of Storage Yard in Port with Long Short-Term Memory Recurrent Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yinping Gao ◽  
Daofang Chang ◽  
Ting Fang ◽  
Yiqun Fan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Arima Model ◽  
Training Set ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Box Plot

The effective forecast of container volumes can provide decision support for port scheduling and operating. In this work, by deep learning the historical dataset, the long short-term memory (LSTM) recurrent neural network (RNN) is used to predict daily volumes of containers which will enter the storage yard. The raw dataset of daily container volumes in a certain port is chosen as the training set and preprocessed with box plot. Then the LSTM model is established with Python and Tensorflow framework. The comparison between LSTM and other prediction methods like ARIMA model and BP neural network is also provided in this study, and the prediction gap of LSTM is lower than other methods. It is promising that the proposed LSTM is helpful to predict the daily volumes of containers.

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