scholarly journals Regional Short-term Micro-climate Air Temperature Prediction with CBPNN

2018 ◽  
Vol 53 ◽  
pp. 03009 ◽  
Author(s):  
Lvwen Huang ◽  
Lianliang Chen ◽  
Qin Wang ◽  
Siwen Yan ◽  
Xunbing Gao ◽  
...  
Keyword(s):  
Air Temperature ◽  
Stepwise Regression ◽  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Climate Data ◽  
Short Term ◽  
Temperature Prediction ◽  
Air Temperature Prediction

This paper proposes a novel short-term air temperature prediction with three-layer Back Propagation Neural Network (BPNN) for the regional application of next 1-12 hours. With the continuous collection of eight real-time micro-climate parameters in the experimentation and demonstration stations in our university, the Multiple Stepwise Regression (MSR) is employed to screen the original historical data to find the parameter factors with greater contribution rate. On the basis of the Root Mean Square Error (RMSE) value evaluating the optimal fitting degree of the stepwise regression, the Levenberg-Marquardt (LM) and the Resilient Propagation (R-Prop) training algorithm are employed to construct a Combined BPNN (CBPNN) with two MSR inputs. Compared with the known micro-climate data sets, the Mean Absolute Error (MAE) is to evaluate the applicability of CBPNN prediction model. The experimentation shows that the MAE is within 4°C in the next 12 hours. This proposal will be deployed in stations in our university for extreme weather warnings, and could be applied to some regional short-term parameter prediction for the future agricultural production service.

Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm

2015 ◽  
Vol 785 ◽  
pp. 14-18 ◽  
Author(s):  
Badar ul Islam ◽  
Zuhairi Baharudin ◽  
Perumal Nallagownden
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Forecast Accuracy ◽  
Back Propagation Neural Network ◽  
Search Performance ◽  
Training Algorithm ◽  
Short Term

Although, Back Propagation Neural Network are frequently implemented to forecast short-term electricity load, however, this training algorithm is criticized for its slow and improper convergence and poor generalization. There is a great need to explore the techniques that can overcome the above mentioned limitations to improve the forecast accuracy. In this paper, an improved BP neural network training algorithm is proposed that hybridizes simulated annealing and genetic algorithm (SA-GA). This hybrid approach leads to the integration of powerful local search capability of simulated annealing and near accurate global search performance of genetic algorithm. The proposed technique has shown better results in terms of load forecast accuracy and faster convergence. ISO New England data for the period of five years is employed to develop a case study that validates the efficacy of the proposed technique.

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

scholarly journals Novel approaches for air temperature prediction: A comparison of four hybrid evolutionary fuzzy models

2019 ◽  
Vol 27 (1) ◽  
Author(s):  
Armin Azad ◽  
Hamed Kashi ◽  
Saeed Farzin ◽  
Vijay P. Singh ◽  
Ozgur Kisi ◽  
...  
Keyword(s):  
Air Temperature ◽  
Temperature Prediction ◽  
Fuzzy Models ◽  
Novel Approaches ◽  
Air Temperature Prediction

scholarly journals Effects of the Madden–Julian Oscillation on 2-m air temperature prediction over China during boreal winter in the S2S database

2018 ◽  
Vol 52 (11) ◽  
pp. 6671-6689 ◽  
Author(s):  
Yang Zhou ◽  
Ben Yang ◽  
Haishan Chen ◽  
Yaocun Zhang ◽  
Anning Huang ◽  
...  
Keyword(s):  
Air Temperature ◽  
Boreal Winter ◽  
Madden Julian Oscillation ◽  
Temperature Prediction ◽  
Air Temperature Prediction

scholarly journals Development of a Seamless Forecast for Solar Radiation Using ANAKLIM++

2020 ◽  
Vol 12 (21) ◽  
pp. 3672
Author(s):  
Isabel Urbich ◽  
Jörg Bendix ◽  
Richard Müller
Keyword(s):  
Solar Radiation ◽  
Weather Prediction ◽  
Software Tool ◽  
Absolute Error ◽  
Shortwave Radiation ◽  
Data Sets ◽  
Climate Data ◽  
Forecast Horizon ◽  
Novel Approach ◽  
Forecasting System

A novel approach for a blending between nowcasting and numerical weather prediction (NWP) for the surface incoming shortwave radiation (SIS) for a forecast horizon of 1–5 h is presented in this study. The blending is performed with a software tool called ANAKLIM++ (Adjustment of Assimilation Software for the Reanalysis of Climate Data) which was originally designed for the efficient assimilation of two-dimensional data sets using a variational approach. A nowcasting for SIS was already presented and validated in earlier publications as seamless solar radiation forecast (SESORA). For our blending, two NWP models, namely the ICON (Icosahedral Non-hydrostatic model) from the German weather Service (DWD) and the IFS (Integrated Forecasting System) from the European Centre for Medium-Range Weather Forecasts (ECMWF), were used. The weights for the input data for ANAKLIM++ vary for every single forecast time and pixel, depending on the error growth of the nowcasting. The results look promising, since the root mean square error (RMSE) and mean absolute error (MAE) of the blending are smaller than the error measures of the nowcasting or NWP models, respectively.

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